{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://www.luxonis.com/logo.svg\" width=\"400\">\n",
    "\n",
    "# Training an Object Detection Model on Automatically Annotated Images\n",
    "\n",
    "## 🌟 Overview\n",
    "In this tutorial, we'll go through the process of automatic image annotation and training a custom object detection model. We'll first create a dataset, set up the training configuration, and then use it to train our own NN model. We'll also validate the performance of our model, export it, and make it ready for deployment on a Luxonis device.\n",
    "\n",
    "## 📜 Table of Contents\n",
    "- [🛠️ Installation](#️installation)\n",
    "- [🗃️ Data Preparation](#data-preparation)\n",
    "    - [🧐 Generating Object Detection Annotation](#generating-dataset)\n",
    "    - [💾 LuxonisDataset](#luxonisdataset)\n",
    "- [🏋️‍♂️ Training](#️️training)\n",
    "    - [⚙️ Configuration](#️configuration)\n",
    "    - [🦾 Train](#train)\n",
    "- [✍ Test](#test)\n",
    "    - [🧠 Infer](#infer)\n",
    "- [🗂️ Export and Archive](#export-and-archive)\n",
    "- [🤖 Deploy](#deploy)\n",
    "- [📷 DepthAI Script](#depthai-script)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name =\"️installation\"></a>\n",
    "\n",
    "## 🛠️ Installation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main focus of this tutorial is using [`LuxonisTrain`](https://github.com/luxonis/luxonis-train) together with [`DataDreamer`](https://github.com/luxonis/datadreamer). [`LuxonisTrain`](https://github.com/luxonis/luxonis-train) is a user-friendly tool designed to streamline the training of deep learning models, especially for edge devices. [`DataDreamer`](https://github.com/luxonis/datadreamer) is an advanced toolkit for generating synthetic datasets and annotating images using the latest foundational models. We'll download our pictures from [`Kaggle`](https://www.kaggle.com/), generate annotations and store them in `LuxonisDataset`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -q kaggle datadreamer>=0.2.0 luxonis-train>=0.2.1 luxonis-ml>=0.5.1 -U"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name =\"data-preparation\"></a>\n",
    "\n",
    "## 🗃️ Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to download and prepare our dataset. For this example, we'll use a publicly available dataset from Kaggle called [`Cats and Dogs 40`](https://www.kaggle.com/datasets/stefancomanita/cats-and-dogs-40). This dataset is originally an image classification dataset. However, we will utilize the aforementioned [`DataDreamer`](https://github.com/luxonis/datadreamer) toolkit to generate object detection annotation for the images. Therefore, our task is to train a model that is able to detect cats and dogs. \n",
    "\n",
    "To download the dataset, we'll run this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset URL: https://www.kaggle.com/datasets/stefancomanita/cats-and-dogs-40\n",
      "License(s): CC0-1.0\n",
      "Downloading cats-and-dogs-40.zip to downloaded_dataset\n",
      "  0%|                                                | 0.00/445k [00:00<?, ?B/s]\n",
      "100%|████████████████████████████████████████| 445k/445k [00:00<00:00, 16.5MB/s]\n"
     ]
    }
   ],
   "source": [
    "!kaggle datasets download -d stefancomanita/cats-and-dogs-40 -p downloaded_dataset --unzip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll move images to one place:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80\n"
     ]
    }
   ],
   "source": [
    "!mkdir catsdogs_dataset\n",
    "!for file in ./downloaded_dataset/catsAndDogs40/train/cat/*.jpg; do mv \"$file\" \"${file%.*}_train_cat.${file##*.}\"; done && cp ./downloaded_dataset/catsAndDogs40/train/cat/*.jpg ./catsdogs_dataset/\n",
    "!for file in ./downloaded_dataset/catsAndDogs40/train/dog/*.jpg; do mv \"$file\" \"${file%.*}_train_dog.${file##*.}\"; done && cp ./downloaded_dataset/catsAndDogs40/train/dog/*.jpg ./catsdogs_dataset/\n",
    "!for file in ./downloaded_dataset/catsAndDogs40/test/cat/*.jpg; do mv \"$file\" \"${file%.*}_test_cat.${file##*.}\"; done && cp ./downloaded_dataset/catsAndDogs40/test/cat/*.jpg ./catsdogs_dataset/\n",
    "!for file in ./downloaded_dataset/catsAndDogs40/test/dog/*.jpg; do mv \"$file\" \"${file%.*}_test_dog.${file##*.}\"; done && cp ./downloaded_dataset/catsAndDogs40/test/dog/*.jpg ./catsdogs_dataset/\n",
    "!ls ./catsdogs_dataset/ | wc -l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name =\"generating-dataset\"></a>\n",
    "\n",
    "### 🧐 Generating Object Detection Annotation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will use the [`DataDreamer`](https://github.com/luxonis/datadreamer) toolkit to annotate the `80` images. The toolkit will detect cats and dogs in each image and generate bounding boxes for each animal. Furthermore, using `--dataset_format luxonis-dataset` will convert the data to the correct format: `LuxonisDataset`, which we need for the training.\n",
    "\n",
    "To discover all available DataDreamer arguments, please refer to [here](https://github.com/luxonis/datadreamer?tab=readme-ov-file#-main-parameters)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!datadreamer --save_dir catsdogs_dataset \\\n",
    "            --class_names cat dog \\\n",
    "            --prompts_number 80 \\\n",
    "            --num_objects_range 1 1 \\\n",
    "            --synonym_generator wordnet \\\n",
    "            --image_annotator owlv2 \\\n",
    "            --annotator_size large \\\n",
    "            --annotate_only \\\n",
    "            --disable_lm_filter \\\n",
    "            --conf_threshold 0.4 \\\n",
    "            --annotation_iou_threshold 0.5 \\\n",
    "            --dataset_name cats_dogs_luxonis_dataset \\\n",
    "            --dataset_format luxonis-dataset \\\n",
    "            --split_ratios 0.7 0.15 0.15 \\\n",
    "            --batch_size_annotation 2 \\\n",
    "            --seed 123"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's visualize the generated bounding boxes for one image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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n/wCzNXf6CoOh2HvaRD/x0V598Qv+Q9EfW0/9mavQtCUf2BpuOptov/QRXkx+JH1WYf8AIsw5wHxhUeTZwB8GSMnGfRq8902O3ghCFyZScAA10nxTuJ7/AMQwQxMSLdXQgfWuL0e3b+0oJZznEgzn0Br2qKtE+Vm9SbXrdktS7Aj+vNez/C7UBe+GEQdVkYflivMPFMkWp3kdraqNvIOK7z4fTQ+H9PaObAU7sZ45OKVTWI4npU0nlfd5JrnPGF4mn+Gp3kcBnyBz6g1IviSxZ2JdePeuN+I12utWCwQP0w2Bz0zWFOGpbZ4/BA0skc5Gd55rf+xn+z2lViSDjGc1FokK4mgnGCgG3NSpefZrh4G5QgkV2t2RDZ0vwoEi6zeE9lTj8TXsiPukJ4A6cV4n8N7mRPEEuchZyqj9a9mLeXwep5rlkxoczAIF70kY+ao5HBbinRvisrlFhjgUI3NR7t1AODVIHG5zPxGOfD9v/wBfaf8AoLVu3Ay2a574hHOgW/8A19J/6C1dE/zGoT99/I9Ksv8AYaPrP/20Rn2x1WaUnGKlk6YquRihvU4kToxKUzJLYpyOAtNDgtTTAnhjyakuPlWnQEYptyNwrRMk7PT+dMtf+uKfyFWarafxptqP+mKfyFWa9BbHA9wooopiCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKpaz/AMgPUP8Ar2k/9BNXapax/wAgO/8A+vaT/wBBNRU+B+g1ueTxsAKbK4NQSPt6Gq5lJNfKK56FyUv8xph5NMHJzTgearULky9KkWoQ3SrEfNJoRi6Nn+0tV/67f1at+IselYmhpu1TVx/03/q1dCgCU22mejmv+9P0j/6SixCpFWmYBRVZJRio5ZvetIyZ5w+R8+lQq/z/ADfdpnm570AqQc1K1kIxNctxqGmzIyjcFYrx3xXn+lm6F+ulgABjhvwr1C4TM77PuquTXI2lvEvih52wuXJye3Fe1h9EZyL1xbLA6RSHKoA4wc81qw3cJhjklwgA4A4rLn1PTLd3e5ZJGAOBuxWdL4h025uLSN7IiEg8+ZxXSRc2dYhkeHcqKIyM5FZek6dJKssq5wmCMd66XUJUn0xY4SMbQRj0xVXw/P8AZbWWOVuw607C5ipLBc3mnv567SjceX1IFR6Ndkp5TxsgPBDLjNZFz4o1H+3XOnTEIispRQDnmlHiK51Dek9u5nUdcjrSHcy/Fml/2fqMc8Dfu5SCwz3Jr1/wfbJZeHYIl5Aycnr1ryfxE806WyTQMh+Qhj9a9c0FsaNEoOetctdaGkLHIfELH9uwkd7Mf+hNXe6O5Hh/SyO1vH/6CK4Dx+2dag/68wP/AB5q7jSJB/Ydjz9y1jP/AI6K86NuY+mzL/kWYc8g8ctLaeLZMAn7Q7v83bntWIiMt6sFoN7nBJboM12fxDia91O2uEiP7tGUt9TVXQtOg09SZgGnIznp9K9WlK8bHy7Wolhov2d1uJfmlbkjqBW05e2hAVQRnOKs2s0QZpZR8oPHNTJLbzhmTB3ZTAPSh7lK1jBS4ke6wwADHjFWLqIyIJSCccYHpVyewW0CS/eK1XmvDZuF2Fw46D3pWsPQ52+0Np3+12hwRzIucfpXNzlWuSBuDJkNu9uteiW4e3aSYqRHJjK1z/iLS4ZYWvLSPHOHxz9ata7kySJfAGzUNdWSElVhZW9K9gkkHmAE5bFeXfDW3solvJIQN+xcYPua9EWRCVI+9iuapKz0KS0JzNz71Kr5FZ4lLdetWEesOcqxeV8VIHzVaNganUcVqmBy3xAOdBg/6+k/9BauoKjJrlfH5/4kcA/6ek/9BautUgrmpv7zPRxH+40fWf8A7aU5uKr7utS3cgXNZwuPmxUOWp56Lm+kDDdUPm8dKEfLDimmUzVtzxUzAGqcTYFThs1qmRY7ax/48Lb/AK5L/IVPUFj/AMeFt/1yX+QqevUjsjhe4UUUUxBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFZ+utt8Pak3payn/wAcNaFZniM48L6sfSym/wDQDUVPgY1ueJm5DnrU0Q3c1jQSFmFbtom5RXzHKdpE24SEDpSjOeaknARjTolDLmnygNz0q3AeKqkcmp4mwtDQ0Z+gsBqmsH/pv/Vq3gwNc3ojH+0tVx3m/q1bisQaiW56Wa/70/SP/pKLRYLVWaXk808ycVTk5Y0XsecOWbmnNLgY5qsBg1YQ4UsOcVdPcCaV0S2dl6lSDmsfRrBLi7nmmKgBvlyR6VbvJsKD2JxXP6jI9jcR3AP7sgkj1r2qGxjMu694bhcPMmcY9q4e7tZ5mhgiQL5eQSOK7ttTnvrdYFlCg9iKzLpRYN5OMyt3rrirmHMS2ryxWASWRvMC8c+1UxcsoCGRg3fBpJXOR83aqLzbZSO9acpPMUl0+SG7eWORvnJOc+tdT4etohbXBkVWm2jBYd81nacq3E+2QjB7f1q1fEafdssUiup/u9qnlBSHa8guYowAAyEfoa7rwtcxzaVFIGJVsgZ+teXX195lwsZcNlM8dq6vwXfgWZg3f6sZ/M1yYlWR1U2N8esG1yLH/Prj/wAeaun024I0a1APItkH/jorj/GMnm6rE3/Tv/7Ma6O0DDSbLH8UKD/x0V4snZn1GYr/AITMOZ/iq5iTTfNdBgY3EDJzmuUXVRFcl5AfL2jnGTXZX9lHfWkkDkdRXI6vYpbxED7rfKK9XDao+VnoyGfxGGhcwgGNeuRzWroVxLPGsseChbHJrkxpciRh4+VPLACuj0GRba3AcgMWPFdbgJSOvmTMD7jndXGanqptbgDaGYEABhxW7LfGI5JyK5fWYxfyErwaXKHMaya4GjTzwg3dAvNPfUIXjaDavluM8jvXLWmmsjo0p+VDk10tto0N1GpBGSw7npStYadzo/B+nwafYrLGinzBg5A9a3ZH/eFgAPpTbCxSztEgj5VelSyxYrz6r1ZstisjHOTUolpipyacF5rmvqMtxyHFWUl4qihxUokAraMrAYXjxs6JB/18r/Jq6XzSOO1cr43fdokP/Xyv8mrqCARRf3mejX/3Kj6z/wDbStdbnHFZ4icPkAVr+TupDbACs29Tz0U1B24xUkanI4FPMWDTlXFNSGSKcVYjOarAZqdDitVIdju7H/jwtv8Arkv8hU9V7D/kHW3/AFyX+QqxXsR+FHmy3CiiiqEFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAVm+Ihnwxqw9bOb/wBANaVZ2v8A/Iuap/16S/8AoBqJ/Cxx3R4BFCVati2cqtQRx7xxVtY9qV8zc9BxsV55Czmp7cnbVd0LvgCtG3j8uLJq07kleT5c1Es/OKdcyjNUAS0tNoYuht/xMNUP/TUfzathpOawdH4v9RP/AE1/q1arHcc1nKJ6Oa/70/SP/pKJy+R1phBNRF9oqWPkZqOU84jbIpi3DRttPQ1cZRsNZN2wj3N71rD3WDGX1wdiyH7oam6vbi80iK5iAIRBkfU0EhrGWM9Spq34fjjudKnsnP8AdHWvWw7ujGZjQ6ebiD7VDO6tHyVU+lZ8v2m7ufNbJ2Hrmr2n2T6HqksU+Qrg4z7n3rq7DRRdJNcLggYKe9d0HY5WcDFFcPPg5x9adPp0ol3HOK1L/UrGwldHmjDhiCNwqOLxRp7oIfOj54+8K2uDWhmPDIu1kZkOQMrV6S2EVlJd3UjfKufXNdQmmR3+kefEQx3DkemK47Xp2mQafEc7chse9RexKWpUga0vS06E5QFcBetdF4NRWWZ1J+ZcfrXPQwppGnkqPmfGfqa67wlZSWulIWUjOe3vXDip6HZSVjP8U5GpRj0g/qa6Wwdjptmp6CJP5Cuc8Wf8hOP/AK9//ZjW/Yxv/Z9sexhT+QrxWrs+qzD/AJFmHFvZFtopJSe9cNrmo75ET/lmGBrr9ecLphB9B/OvObqU3EgGf3WcV7GDR8pUNS3aZoyU5RuRzVezmkmnyCRhsYz71tWcUMWjp5ZG7aO9ZtjAYJSXUgMx5/GvRa0MOY17kmOFWfkYrLMnmSgIOprSvHVrUwjnIwMVXt7dllRsbSMdRUqAnIqalI9pH5ZUAvxXR6BMrCJTj7oqn4ht4bm0imdlBGSeazdEumDFjwinaDWU9DWDuerW8mVFSyEYrI02Qm3Rz3q40obvXl1dzrWw8Yyaa1QeYFJ5ppmya57ajJtxzUyZNQxjfitCGMBK0Ubgcv41GNFh/wCvlf5NXT5IGa53xyuNDh/6+V/9BaupZcihLVnoV/8AcqPrP/20gEpzipQxPWozHzT1XFKSPPHbM01lwKk34FQSPuyKjYENL4p6vmqjkg1LCGarUij0TT/+Qba/9cU/kKs1W08Y021H/TFP5CrNe7D4UeZLdhRRRVCCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKztf8A+Rc1T/r0l/8AQDWjVDXBnw/qQ9bWX/0A1M/hY47o8WtF+UZq4VyuKZbwsB04qwB2FfMNHoXuRwW4zkiku5fLTaKmLmMdOaoXG6U804hYoMzOxNPjCpyakMaxqc9aoSytuwCMVsthEmhr5l/qPvKP5tWlcjy+lZvhs4vb/P8AfH8zWtfjpipsehmv+9P0j/6SinETIeavIAqiqKYiIIqRpwRweaFE865c3AqRWbdw+ajAetKtxg4NSIys/wBaTQyAWp8sntjFReHX8vWpoWOFL/0rQUscrkbaw7ZgviB4/Vz/ACr0MO9DOSOl1+wW/tDcgbZl4GPQCn+FNUaOxa3m+9GAOe9X1AvYwXYbsbcDjiuc1NX02/UxKeSc4Fd0Wc7jqee+MNOaLXJZoyZUbnDcDJJrFsrR7qbabdIzngqa9N8TafJc2qPAmCQCdwz2rmtGs7yLVUEsX7vd12VrzFuGh10PiC30Lw+tqjb3YDOQRjjFcjp6yXOrtcMSRKR1qfxBZyWt4pkPysuQPqan0NYotzOCGXBXJqJMlRG6yoaeC1UcsVY/nXp9taxw2KxqANozXmnnLc+IIUGCMc/nXqkgEYAHRhg15+Idzpgjz/xcMapH/wBe/wD7Ma6fTwP7Lss94U/9BFcx4vGNWTHTyP8A2Y1vWhf+zrQKekKH/wAdFcEVqfT5h/yLMOReIIhJZMAO1eUJ5hwvbzCP1r16dHltn3c/hXmclqsV+/bZ84zXqYV2PlqsTqtM0eWXTomBONopPE80Ohwxq8KktjJI6ZHWtvwjqcWo2ZtWK+amFHQVn/EUx3FspKFimOF68A16TZxNanFjxbaW1xEY1W4H8e8EYr0fQYtP8S2rXNmQXCEFNuACB714fFBPNdFooyoJ53LXuHw6tUtdEcDKnczNk+wouFjnfEul3EGnyoXYEqdvNc28725trOEZkYqzdvrXT+NNZje+8vOVjJ24x6CsDwtH9t8QC4cZ2ggfnXNUZvTR6nYxEWSAjBxTX3K+K0CFEBccHFZk0w3V5tQ61sNYMTRtK8mptwpkrZFc/UZYt5QKvpP8tZMK1aQsDW0RGZ44bdoUH/X0v8mrqu1cl41OdCgH/Tyv8mrqw1C+Jno1/wDcqPrP/wBtHYoxinqQRzSFlzUyPOIHBqu2RVpnBOM0x416ismxopMSWrQswO4qs6Y5xU9q+Dg0JlHf2X/Hjb/9cl/lU9QWXNhbn/pkv8hU9fQQ+FHmS3YUUUVQgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACqOtHGg6ifS1k/wDQTV6qWsjdoeoD1tpB/wCOmpn8LGtzyq2dGi5qREG7OOKgiiPRavhNkXPWvmmjvRUuSgGeKz3kQA1PecA1kSlsHBqdiitfXJBAFQQBpGzTynmtgir9tbiMAmrUiGVtAG281L2kH82rWujkDNZuhIWvtVx2lH82rQugTiqTPRzX/en6R/8ASUUrg4AxUCMc81aaMyYwKHgCqD3xTTPNaZSmYK4q5bbWxzWfdglwADV7ToicK3U9DRa40a0dn+5Zyegrl5UW31lZgeSSa6UztJbyxowBCnrXI6gXL/LnenBPau+grITOltLxo5xliBVm9ZLq4B2hsVm2xjuLUSg8qOefSremkuHlXhRj71dMGZuOpuXFkk3loVAXYDUEGm27zmJIl3A4zitO0uItQsSFkQSqSOT2FJHtsYGmkkjU4zkmtbmt1Y828aAf2zFAT8qxg/kTWbJcRCBTFgE+lV9U1H+1NcnZTnazLn8ahn2xKgAPXvUtnO9yrBeNbeIIWz1BP617VZzm6tEkNeEgn7T5pBLBsDHpmvXdDvi+lR475/nXJWVzaLMPxkANXj/69/8A2Y1sW0vl2Fr/ANcE/wDQRWF4scyanGT/AM++P/HjWzaLvtLTPA8lOv0FcKXvM+mzB/8ACZhy5FL5sbg15z4jQw3z+XwcDOPSvSzGlvAWLDn0NefeIVJvNwxgkbvpXZQbTPnJK5n6DcXVnqEM9vIwByXAOO1afiy21eeMXMMkjRkAEZHpzWLYBRqmN2IyTxnnpXVXupf2dakIQwK/xc9q7+a5ySjqcHplnfXF0scMru+eV6V6jFc3OgeHiJk8t24zn1FcNb+JbtZA0cUIB7iPmtC58R/aNMKTnLFun4U+YzszDurj7XLI8xy3vXT+CbYJKZccZP8ASuTht2m3OxAD9BXonhqz8iyXoMkH9K5qkzeCOvkYmNgvTFY9yShBrUyfL45DVmXQLOFrjmdKJIpdxx6VMcGq/l7OlKH7VgM0IAKkk+U8VUgk5Aq8AG61omJnO+L5C2jQg/8APwv8mrrg+DXJ+Mo9mjwn/p4X+TV0RfDGhPVnoV/9yo+s/wD20tmXjio2Zj0qJcsamyAKhs89EPzb6nL4Ayahdxniqk1zhsVmxmmAHFOjiwaqW02Vq0JOaaKO+sP+Qfbf9cl/kKsVX085021P/TFP5CrFfQw+FHmS3YUUUVQgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACqmqjdpF6PWBx/46at1V1LjS7v/rg//oJqZ/Cxx3R5pFCFbkU+5YBOKGcE8EVn38xRetfOJnoNFC8nCE7u/SqsUfmZY9Krzs1zJgZ4rSiXbCF70mrgiobcCUEDrVrZhRQwwV4pzngUlEqxQ8Pf8hDVv+uo/m1XrojdVDw+cX+rf9dR/Nqu3DAyUz0c0X+1P0j/AOkoSJc9qWVeOlSxBQtLLjZ0pJ6nn2MsxhpOlXkAiTK9cVWj5l6VYkPybe9dFNXZnJWKbXUdojNKR82RzVeawe6s2uoVynU4qzJZq9qXmBOMnip7S9WPTjCq/JgdRXdFaGdzl7G8kt1dWJxyMV0uj3KS2xTONwFY02kO8DTfdyx74qmJJrXYEJwOpq4MGzdNxNaXBe3BRSccetZuv6jqE9rLG07gY46VYNzJcWoDGMbfm46msa/Y3UanDgjqDxWrZk2ZWm2ghmLDktkk+9JrEjIAFHIq62LRlA7rms95DdXjqRxx2qbgh+m2by27Suveuy8P3Df2eintn+dQWdtHHpgwBg4/lU9qEtoY1GBk1lU2NYlPxIc38Z/6Yf1NdPp1u9zp9rjtCn8hXLeIDm8iP/TD+prptHumisLdeP8AVr/KuGPxs+mzD/kWYcmvLcxx7WauL163wCw6V29yTIu5q5TXE3QPgHAB/lXTA+fexyem2xlvg3YGtXWRGYijEZ2/0qvo08UMjFh0PWq+salA7NzXVA5JlC1UJHgDpT7vTytr5jHvnpVJL9RbSMo5AGBitG31Nb63ETI3/fNVIUSPTd9w6N0Ga9K0rc0McajsK4nSoB9rjiC4GfSvS7SFbdIgByQK5ZG8UaUUJTfGR90VQePdMxPY1qTzCI46setUpjjkDrWbRaI2jHl5qvt5qSSXapGap+cS1YtGhciBHNWElOaitvmWrAi5qlElmN4xbdosP/Xwv8mroZeCa53xiMaND/18L/Jq6CU0Je8zuxH+5UfWf/to6J+aklb5eKqBiKaZyOKiSOFCO7A1XbLGrBIcVGQAaysMnt3KirKy1TQ81ZRMigLHpWmnOl2h9YU/9BFWqq6ZxpVmP+mCf+girVfQw+FHmy3YUUUVQgooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACqup/wDIJvP+uD/+gmrVVdT/AOQTef8AXB//AEE1M/hY47o8uYBJS1YWq3waTbmtW9nVARmuYuE82bdmvnEj1JIuWY/j9ea0VwxrMV/LgUDqBU9nIzNzSbMy8y7RVct81TzSAgCoVj3HNCZVzO0RsX2rH/pqP5tU87Zkqlpj7L7VR6y/1apmbMlVbQ9HNH/tT9I/+kovW/WrRwSPpVSE1ZJAXNSo6nnpkaw/v91PliAUyt0Bp4J8vcKSZS9qcnAOK66SJepDJIXhMIX5G4p/9miS1iSIZZVxgVm3OpNHLFGqZUsBnFbVhIykSZ69jXUnoZ8pLLYb4liaLIrnruwCXctuI8IxxXZw3ZRSXVST04qhqtuJY0kUKJGGfShMlo8/uVa3lyFOFNNe6Nwm7u1dHJaJcwsNozkjpWf/AGKYwg7CtUzNo56dd8RLpkg4BqfTdOygmmjxn1rqL/RUt9JWQAFi6n8KguIw+noYgoxnpxS6gkVZWa3gEKD923zH61mrf7540c/xVald1tSHIJyMc9qybq2dpYmj/vVE9jaJq66wae3I6fZh/M1tWTiLT4Hbr5a4/Kuf1RWU2wbr9mH8zWp57R29rHjIMafyFcMfjZ9LmH/Isw5qxXT3IOTwO1Vb63M9tKAPvKRUZjkgQSg8EZxmhr/fHwOPpXRA+fexxKWxglngPriucv0LE98NXVX5f+1JHA4LGufkhZS5YcHNdcDlmUbe2abgHH4Vv6fALZctziszTbSSWc4OBn1roYYNjBG5zxVSFE2fD8Qup/N29cV3SBgU2HouK5vSo47GJUA5PtW3AJWjMnbNcsjaJZBd71tx9KtzJmPGazYXZ7hj34qy05DbWNS3oUilPCU5HeoEX5+a0ZUyMjmqgiO+sWzQvW46Cr6x8VVtwqgZ61a8wAU0yWc54zXGkQ/9fC/yat6bG2sDxk+7SIv+vhf5NW5NkZoT95ndiP8AcqPrP/20ZnANVXYg1KHycUjoGwalnAhISTUrRljmkiQA1eiQNis2i0V4oyKtqMDrSSKqd6Yvzd6hlpHpOm/8gu0/64p/6CKs1V03/kFWf/XBP/QRVqvoYfCjyZbsKKKKoQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVLVzt0S/b0t5D/46au1Q1z/AJAGpf8AXrL/AOgmpn8LHHdHiWpXQLcGqkbBlz3qG/P7zrU1umYq+ePUYpyVFW7UYFVhydvpVuJSozWchJFnbuYZqbbsTIqCN8uBU1zJtiqYoqyOd09h9u1PPeX+rVbMZDBu1ZNnKRf3f+1J/U1uswaEY68VqtjszX/en6R/9JRJERvHpU6gO5XHFUoyQwFadquZBkdqW7PPELBI0XcBk80ahNEloqAjeQMHPSq0zK1yibsfNUV9sEqRlicg16FOOhLZag05LiI7XBKjcB71ZsLc7mjlHQ9azNEuGinbe5Knjr71vo5EhkCjbmrSJbIpb2KJfnwBnAJNNgnjvZcmQSrH0AqtqGns6sSeAN2M1WtNQWxh2RwqXfuVq7EtkymMSvGjBDy2DUc86oqrMck9+lOaWAXokfjKZIAqe5+y3FsHA7dcVSRNyLUnSWwQK4ZeMgVmGATWqxW8RA5z3pIyGLJvOMnGTVrT827srYwelSx6GZc6YyoGKE4GMVWitGWJSww2eK7eGGBoi0hByelYGqW5S4V48bAaVhpnOaxKJLiMDqkO0/XJrTS4ggS0EoGTGnf2Fc5M7SXdxns7AfSpNTMhhglBOFRe/oK56Ub1JH0mZO2WYY7G4ngkt1wQAR61Q8qIodpGPWsOw1D7RBsZjkcVbtpWaRoixxjg5rflPAUtCprFubWD7SBuBGcish0S5t+BtY11Oq2sk2liFBuO3HP1rnZLSVU2qMFRmrV0YyI7SCK3hJyBIOprS0iA3c3mOuUU/wAqq2ei3d+Q4JAbr81dTBZLp8AhUfMRnNJthEsQ2klxdLIDtjB4GK3ZLhLW3ETcA1kw3P2e1G/hgO1Yur6u0hVQcDjpWbRodXB5Y2vHyW7ioWybgmUZHNVdGnD2kXJI9TWtqEKi2Ei9TipaGhkT7lHpSuQDxVGObZGB6U1rrJ61k0aGmjDIqVpPSs+CXd3q7Gu6mrEMw/FrZ0iL/r4X+RroZWyhrA8Xpt0mL/ruv8mrbc/Iaj7TPQr/AO5UfWf/ALaUgzebyeKtZGBVHJ8yp8k4xU2POJ1fBq7C2RxWUWINX7Rs4qGWh8+aS3Rs81I4y1WIlGKSLR6Dpv8AyC7T/rin/oIq1VbT/wDkG2v/AFxT+QqzX0EPhR5Ut2FFFFUIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArO1848OaofS0l/wDQDWjWZ4jOPC+rn/pym/8AQDUz+Fjjuj54vJ902M1pWr/uKw5Pnm61uW0f7kV8/JWPUZJbjM5+tazR4jzVG2ixIK2Cg8sVkyooz4UIkJNF9IFixVkx7eaxtWnKjAqoocjHshuvbjH9/wDxrdQFVBPSsbRQJLybPdh/WuguUCRcVXQ7M0X+1P0j/wCkoIAJZQRwK2EURKHP92sXSyWk5rR1C6EcGB1FOGrOBo564lb7asoBK7ulWLkKzRyM4G5cjPaq1vcxSzyRsBkDjik1pWitonUkfL/Wu+OiM5Im0uWMRKz9d54z71vT3+2zBicLx35rjLa7Vg8in5Qv61tWam8sGbccACriYsml1ea7i82MEEfL0z0rBOs6o16wBwkRxjYOa0IyLe2Kjn5jWdK7Ik0oUc81dyDbsLyC+f8Af/K+McnFXr2zmFohtzlMHOBmuUsdStxMWYAHGOnetrT/ABC8VwYplBgJwCSapMRmX95BHOkan5wBk571bErMqN0A5PvXP+JY1i1eN4D8rgNx7mtKS5YW8agcnNZN6j6G5HctNho1YbRtI/rVXUbmWOIRlgSeOlLp7lYSc/Mahu4RLZySlzuVSaaY0c3ICsz5OScmn6hqKQrFaPEzExr8wPHIqlbSPI0+5icSEDmma3Exu4SrEny0/wDQaxo/xZH0uZ/8ivDfMmtrfY/mIeDzit21haRkC8c9aq6JGPKxKoPTrzXRJJb2qqSi4z6V0HzdyEu1rJguGHpVMoZmBC8luT7VqG6s7mThVz/u1C37tSUQYqhFtGFpAFjXGRUUjNJEWx845zSQkzwZdscVsW1haS2u4ztkdsClYd7HITSzysrMCd3YCqsVpLd3GHUqB6iutlsoEPmKAVHPSs+aWJbobFA+XsKXKNSL1lafY7ZFHIrZkIlsQueRism0ufPQDHFW/Mx8uazkjWJRlYBjxiqb7gTgVavTsCn1NJAqyJk1zyNBlpIQwzW3bygDmsTISTAq2k2B1qGSVvF8yyaXEB/z3U/oa2UlDcYrmPEbFrCPn/lqP5Gt22JBwalPVnoV/wDcqPrP/wBtLLwjqKIYzk8ZqfgrUkC4LcVTPOKFyu3mn2kwGKlvUyKpQqVYVkykbSEOuaWOUK+DUdtytRyKVk60kWj0/TjnTLQ/9MU/9BFWaqaX/wAgiy/64J/6CKt19BD4UeVLdhRRRVCCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKyvE/HhPWf+vGf/wBFtWrWX4lGfCusD1spv/QDUz+Fjjuj5rhy0orpbYYiGaw4Ytsma3bRt+BXgSPVLsAwwrQL/JVZEAAokk2isjSNiVm+U1h36CR8Zq5JdbePWsyOfz7zbWkUTIr6MhW+uePuuB/Otudt64rO0kAXupn0l/q1aC/O9FtDtzT/AHp+kf8A0lElkuw8daj1O5VZkRuMrmp4RtkFc9rkrm8yCeMj9aqmtThbsFtADfBx3IzW3r0Cto4cDhQAT+NYGlXQ887+tdRDA2o2M1u5+VjkfhXUmQ2cHaLHGvlBwVJ65rodPuVt7GaPIxkYPrXIKHs7qeGUYKgkfnWlp0slzAy5OBjvWsdDGSLj3wAcZGeTioopjdwPGCM+lYt5ceTITnk8VHaXc1vKJOcNz1p3IsbA0p5FBAwQ1XbyylXT3IdQVFP07UPtJEYUZPematcm0LxO5YNxg1S1JloUdPhfUZ183kpx+Va14EhkwPur3q9penRxWP2pDyy9MeormNUmuJJWjBKq3HBrKSfMUloXY9SAkwrAjOOKfql+q2UihSMqRntWFbgQTrE55Ybs1f1aMyabI6k7XUhPrVIaRk6UCIpiTndIx/QVNqTKt5EwUkhE/lUtvZvZWkKyfekjEn5//qpLlQbobu6L/KsaT/eyPpMzX/CXhza0x/Msi4IXGOtVry7k2fM68GohMYLBkTvisme6zw55rpPmbFuHWHhnOxGYA9QK6vTJpdQtWOMDB5xXJ2FossLOvfFddp4+w6AzA4O5v5Uc1ikiFkZQ0bOBWULiSKcrltueueKempLcgt3WoPPXy2LAZzxVxaZMonUW83mWSRhwzEcgVSSHMjl+DyOaXQkJCyN0pt1I32sgfd3f1obsEYmvpwEVuqYyafLnzciktwNqbetOc/vCO9YTmdCjYbfKHtFI7ZJqhBMI0OSAKv532sqnrtNYMz/N5dYvUdy8jGSSraoags4Twa01jG2smxGF4gGNOj/67D+Rrogm05xWB4jXGnR/9dl/ka6kp8gNZ31PRr/7lR9Z/wDtpCrnOK0LXkGqDcVdszQpNs88W6T5elZhQh/atq4GVrLmwAabBFm1yBTbhsNk1Dbz4OKkufmXNSWj1DSTnRrE/wDTvH/6CKuVS0f/AJAlh/17R/8AoIq7X0EPhR5Ut2FFFFUIKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArN8RDPhjVh62c3/oBrSrO18Z8OaoPW0l/9ANTP4WOO6PnqTEY5q7p8m7kVVvoTirGkxnPNeDI9ZRNsSfKKrzS0XTeWnBxVDz92QTmosUkLMwJB9Kr2kYS5Mn1ofO7HY0GQRjbjmqsRJ6jdPl23Wpf7Un9TWjavlqxtPybm8z3f+prUtgVk60lsd+Zv/an6R/9JRpA/PXMa7zdgr+P510x45ArldWDSzsFODmtacdTiexYs4IfI8zd831rSj1NrYxbOmMGuajaWDCsTg1o/K8AYPjA6V0JGMmReMLNJJEu4RwcZx9KzdGjlnJjjHLe1XNQvXkiWAjcuemasaRE1tcwsg8sEdqtE7mNd6bsuNjn5s5qxHZMyKNnC+1dZremW0jCeJQH2jkCuYvL+Sy2wqPmbvmmjN6G9CltYWWcfMR1/CuYlh+23UsgcMM9jmnXc9xIUBlYxkDK1c0a0AlnGMB8batEM6jQUX7DsZgfbPtWfr9im0Oo24zUkENxZxGRGON2Kw/EOuSLBJC4+dR1zRYaZWi037VfxgnCheTmtDUlt4ZLO3VwY1k5+aofD90lzalzy2MfpVa80+Z44yXO5GJJpSLiWtZdXuIdn3VgCj6ZNZeoRnzoWHdV/lV29Vk8gMcnyB/M0XUTTNZKF7ru9xiuWm/3kj6bMf8AkWYcEtzJacegrFm0yW5BcdR9a9Fg0+3tbNZXQbSucEVyaXi3V0qW42JvwQO/NbuR83Y0PDOiyGz+b0HrTtaMltGtop+82PzrSt7xrOVIEYru9Koa8Ge4gkHJ3rWTkykjOOjSWtg8gByR71U8qVUAdT1z0rrPNJgWOTkOOAaebWC82hFGVxmqjMmSuQacsgsIwq849KZN+6U7x8xaukht44rNVVAGArGntmubkg8gVpzXJSLNhmICR+hpDKGumbtzUuzbBEG6ZNU1kTz2HGOaymzVFiE73cDoRisSaIjUWHua0baRt4wxHNRz27fa/M9c0lsNmtZxr5INWyg28VWswxQjsBTnmZDtrJoI2Zi+JlI06P8A67L/ACNdNuwgBrnfE6n+yImPedf5GugmUge9Z21Z6Ne31Kj6z/8AbRjcmrEEgjIz3qtGCx5p0ykbcUcp57sahdXSsq9GM4q1Cx2cmoJl3k55oaJ0K1sMNk1elG9MCoYotvapGyBxU2HdHqGkDGi2I9LeP/0EVcqnpP8AyBrH/r3j/wDQRVyvoIfCjzZbsKKKKoQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABWfrvPh7U8f8+kv/oBrQqnq43aLfL628g/8dNTL4WNbng01s8g6VbsLVk7VurZKV5FOSBEOK8NxZ2+2Of1VJPL+UGs6ytpHOWBrtnsY51xwahXTFibpQosPbHMXFq4XIU8VAtm8iliDkV2klijKBgGq/wBgUNtxgVViHVucVpERe9vBjo4z+ZrVMLo2Qp/KjRoVXVNZHUJPgfm1dNDbwvFubA5xipgtD1M0n/tb9I/+ko5W6vWt48sP0rn7uU3L7o+Cea6XxatvbGONCG8wkE+nFc5LGCqCDhgvJFbxsjljJNGfdtPAgLDP0FQ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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='catsdogs_dataset/bboxes_visualization/bbox_5.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name =\"luxonisdataset\"></a>\n",
    "\n",
    "### 💾 LuxonisDataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thanks to the `DataDreamer`, our dataset is already in the correct format: `LuxonisDataset`.\n",
    "\n",
    "We can check the dataset's information and visualizations of the annotations to verify that the data is correctly loaded and split into subsets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╭────────── Dataset Info ──────────╮\n",
      "│ \u001b[1;35mName: \u001b[0m\u001b[36mcats_dogs_luxonis_dataset\u001b[0m  │\n",
      "│                                  │\n",
      "│ ╭─ Split Sizes ─╮                │\n",
      "│ │ \u001b[1;35mtrain: \u001b[0m\u001b[36m56\u001b[0m     │                │\n",
      "│ │ \u001b[1;35mval: \u001b[0m\u001b[36m12\u001b[0m       │                │\n",
      "│ │ \u001b[1;35mtest: \u001b[0m\u001b[36m12\u001b[0m      │                │\n",
      "│ │ \u001b[92m─────────────\u001b[0m │                │\n",
      "│ │ \u001b[1;35mTotal: \u001b[0m\u001b[36m80\u001b[0m     │                │\n",
      "│ ╰───────────────╯                │\n",
      "│ \u001b[3m            Classes             \u001b[0m │\n",
      "│ ╭────────────────┬─────────────╮ │\n",
      "│ │\u001b[1;3;35m \u001b[0m\u001b[1;3;35mTask          \u001b[0m\u001b[1;3;35m \u001b[0m│\u001b[1;3;35m \u001b[0m\u001b[1;3;35mClass Names\u001b[0m\u001b[1;3;35m \u001b[0m│ │\n",
      "│ ├────────────────┼─────────────┤ │\n",
      "│ │\u001b[33m \u001b[0m\u001b[33mclassification\u001b[0m\u001b[33m \u001b[0m│\u001b[33m \u001b[0m\u001b[33mcat, dog   \u001b[0m\u001b[33m \u001b[0m│ │\n",
      "│ │\u001b[36m \u001b[0m\u001b[36mboundingbox   \u001b[0m\u001b[36m \u001b[0m│\u001b[36m \u001b[0m\u001b[36mcat, dog   \u001b[0m\u001b[36m \u001b[0m│ │\n",
      "│ ╰────────────────┴─────────────╯ │\n",
      "╰──────────────────────────────────╯\n"
     ]
    }
   ],
   "source": [
    "!luxonis_ml data info cats_dogs_luxonis_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!luxonis_ml data inspect cats_dogs_luxonis_dataset # NOTE: If you are on Google Colab this command will not work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name =\"️️training\"></a>\n",
    "\n",
    "## 🏋️‍♂️ Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name =\"️configuration\"></a>\n",
    "\n",
    "### ⚙️ Configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have prepared the dataset and are almost ready for the actual training. The last step is just to set up our training configuration file. The whole training process in `LuxonisTrain` doesn't require any coding. We advise you to take one of the base configuration files from [here](https://github.com/luxonis/luxonis-train/tree/main/configs) depending on the task and then edit it to fit your needs.\n",
    "\n",
    "In our case, we are training an object detection, and thus we'll take a [`detection_light_model.yaml`](https://github.com/luxonis/luxonis-train/blob/main/configs/detection_light_model.yaml) config as a starting point, which downloads pre-trained COCO weights, making it ideal for fine-tuning. There are many parameters that we can change, and we advise you to go through the [`documentation`](https://github.com/luxonis/luxonis-train/blob/main/configs/README.md) to find all of them. In this tutorial, we'll only go through some basic ones to get you started on your journey.\n",
    "\n",
    "#### Model\n",
    "In this section, you can either choose one of the predefined architectures (all of them listed [here](https://github.com/luxonis/luxonis-train/tree/main/luxonis_train/config/predefined_models)) or create a completely custom neural network by connecting different nodes, losses, metrics, and visualizers. We'll go with the predefined `DetectionModel`.\n",
    "\n",
    "#### Loader\n",
    "This section of the config refers to the data loading. You can either set up your custom `Loader` or use the default one with the `LuxonisDataset`. In our case, we'll go with the second option; all we need to do is set `dataset_name` to `cats_dogs_luxonis_dataset`.\n",
    "\n",
    "#### Trainer\n",
    "In this section, we can set up everything connected to the actual training. You can change preprocessing, batch size, epochs, add callbacks, augmentations, change optimizers, schedulers, and more. Please refer to the [complete documentation](https://github.com/luxonis/luxonis-train/tree/main/configs). \n",
    "\n",
    "#### Augmentations\n",
    "In this tutorial, we'll leave most things as they are; the only change will be adding some augmentations. `Luxonis-train` uses [`Albumentations`](https://albumentations.ai/) for augmentations by adding custom ones like `Mosaic4` and `MixUp`. You can use [this demo](https://demo.albumentations.ai/) to experiment and find those that work for your specific training run. In this training run, we'll add `Affine`, `HorizontalFlip` and `ColorJitter` augmentations, but feel free to edit this.\n",
    "\n",
    "#### Callbacks \n",
    "Callbacks are very helpful when merging more functionalities into a single training run. For example, we want to train the model, evaluate it on a test subset, export it, and create an archive. These steps can be defined through the config and done by a single call. We won't use them in this tutorial for a more straightforward explanation but feel free to set them up independently. You can check out all the available callbacks [here](https://github.com/luxonis/luxonis-train/tree/main/luxonis_train/callbacks)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is a starting point for our config. As mentioned before, we already made some changes to it, so it works with this tutorial (model name and dataset name change, addition of augmentations), but feel free to edit it further and make it your own. When you are done editing, you can execute the cell, and the file will be written and ready to use.\n",
    "\n",
    "**Note**: In case you don't have enough computing power on your machine, you can either use [Google Colab](https://colab.research.google.com/) (with GPU enabled), or you can try tweaking the training hyperparameters (such as lowering number of epochs or batch size). However, please be aware that bad parametrization can result in worse performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting cat_dog_detection_config.yaml\n"
     ]
    }
   ],
   "source": [
    "%%writefile cat_dog_detection_config.yaml\n",
    "model:\n",
    "  name: cat_dog_detection_model\n",
    "  predefined_model:\n",
    "    name: DetectionModel\n",
    "    params:\n",
    "      variant: light\n",
    "      loss_params:\n",
    "        iou_type: \"siou\"\n",
    "        n_warmup_epochs: 0 # No assigner warmup\n",
    "        iou_loss_weight: 20 # Should be 2.5 * accumulate_grad_batches for best results\n",
    "        class_loss_weight: 8 # Should be 1 * accumulate_grad_batches for best results\n",
    "\n",
    "loader:\n",
    "  params:\n",
    "    dataset_name: cats_dogs_luxonis_dataset\n",
    "\n",
    "trainer:\n",
    "  preprocessing:\n",
    "    train_image_size: [320, 320]\n",
    "    keep_aspect_ratio: true\n",
    "    normalize:\n",
    "      active: true\n",
    "    augmentations:\n",
    "      - name: Affine\n",
    "        params:\n",
    "          scale: [0.7, 1.7]\n",
    "          rotate: 20\n",
    "          shear: 5\n",
    "          p: 0.3\n",
    "      - name: HorizontalFlip\n",
    "        params:\n",
    "          p: 0.3\n",
    "      - name: ColorJitter\n",
    "        params:\n",
    "          brightness: [0.8, 1.2]\n",
    "          contrast: [0.8, 1.2]\n",
    "          saturation: [0.8, 1.2]\n",
    "          hue: 0\n",
    "          p: 0.2\n",
    "      - name: Sharpen\n",
    "        params:\n",
    "          p: 0.3\n",
    "\n",
    "  batch_size: 8\n",
    "  epochs: &epochs 70\n",
    "  accumulate_grad_batches: 8 # For best results, always accumulate gradients to effectively use 64 batch size\n",
    "  n_workers: 8\n",
    "  validation_interval: 10\n",
    "  n_log_images: 8\n",
    "\n",
    "  callbacks:\n",
    "    - name: EMACallback\n",
    "      params:\n",
    "        decay: 0.9999 \n",
    "        use_dynamic_decay: True \n",
    "        decay_tau: 2000\n",
    "\n",
    "  training_strategy: # Fine tuning params\n",
    "    name: \"TripleLRSGDStrategy\"\n",
    "    params: \n",
    "      warmup_epochs: 2\n",
    "      warmup_bias_lr: 0.05\n",
    "      warmup_momentum: 0.5\n",
    "      lr: 0.0032\n",
    "      lre: 0.0000384\n",
    "      momentum: 0.843     \n",
    "      weight_decay: 0.00036   \n",
    "      nesterov: True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name =\"train\"></a>\n",
    "\n",
    "### 🦾 Train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To start the training, we need to initialize the `LuxonisModel`, pass it the path to the configuration file, and call the `train()` method on it.\n",
    "\n",
    "**Note**: LuxonisTrain also supports all these commands through usage of its CLI ([docs here](https://github.com/luxonis/luxonis-train/tree/main?tab=readme-ov-file#-cli)), no code required. We won't use them for tutorial purposes but feel free to use them when you do it yourself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from luxonis_train import LuxonisModel\n",
    "\n",
    "config_path = \"cat_dog_detection_config.yaml\"\n",
    "\n",
    "luxonis_model = LuxonisModel(config_path)\n",
    "luxonis_model.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`LuxonisTrain` has also already implemented automatic tracking of training runs. By default, `Tensorboard` is used, and to look at the losses, metrics, and visualizations during training, we can inspect the logs. If you check the `output` folder, you'll see that every run creates a new directory, and each run also has its training logs in the `./output/tensorboard_logs` where the name of the folder matches the run's name. To make all the subsequent commands work automatically, please set the name of your run below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "RUN_NAME = \"<YOUR_RUN_NAME>\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext tensorboard\n",
    "%tensorboard --logdir output/tensorboard_logs/{RUN_NAME}/ # TODO: Change the name of the training run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name =\"test\"></a>\n",
    "\n",
    "## ✍ Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have a trained model that performs well on the validation set. The next step is to check its performance on the testing set, a collection of images we've kept hidden from the model. It should only be used to evaluate whether the model is good objectively. Since this is an object detection task, we use the mean average precision metric to check the model performance quantitatively.\n",
    "\n",
    "If you check out the run directory, you'll see two folders inside: `best_val_metric` and `min_val_loss`. Both have checkpoint files generated during training based on best validation metric performance and minimal validation loss. For evaluation, we'll want to use one of these checkpoints; we recommend that you use one that has the lowest validation loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "         <span style=\"color: #800080; text-decoration-color: #800080\">/home/jovyan/dai-ml-training/output/182-lavender-lizard/min_val_loss/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">cat_dog_dete</span> <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                        </span>\n",
       "         <span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">ction_model_loss</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7.</span>2226_69.ckpt.                                                  <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                        </span>\n",
       "</pre>\n"
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       "\u001b[32mINFO    \u001b[0m Loaded checkpoint from                                                            \u001b]8;id=713518;file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py\u001b\\\u001b[2mluxonis_lightning.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=494124;file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py#986\u001b\\\u001b[2m986\u001b[0m\u001b]8;;\u001b\\\n",
       "         \u001b[35m/home/jovyan/dai-ml-training/output/182-lavender-lizard/min_val_loss/\u001b[0m\u001b[95mcat_dog_dete\u001b[0m \u001b[2m                        \u001b[0m\n",
       "         \u001b[95mction_model_loss\u001b[0m=\u001b[1;36m7\u001b[0m\u001b[1;36m.\u001b[0m2226_69.ckpt.                                                  \u001b[2m                        \u001b[0m\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">INFO    </span> The following callbacks returned in `LightningModule.configure_callbacks` will override    <a href=\"file:///opt/conda/lib/python3.11/site-packages/lightning_utilities/core/rank_zero.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">rank_zero.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///opt/conda/lib/python3.11/site-packages/lightning_utilities/core/rank_zero.py#64\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">64</span></a>\n",
       "         existing callbacks passed to Trainer: EMACallback, ModelCheckpoint, RichModelSummary,      <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
       "         TrainingManager                                                                            <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">               </span>\n",
       "</pre>\n"
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       "\u001b[32mINFO    \u001b[0m The following callbacks returned in `LightningModule.configure_callbacks` will override    \u001b]8;id=732945;file:///opt/conda/lib/python3.11/site-packages/lightning_utilities/core/rank_zero.py\u001b\\\u001b[2mrank_zero.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=662718;file:///opt/conda/lib/python3.11/site-packages/lightning_utilities/core/rank_zero.py#64\u001b\\\u001b[2m64\u001b[0m\u001b]8;;\u001b\\\n",
       "         existing callbacks passed to Trainer: EMACallback, ModelCheckpoint, RichModelSummary,      \u001b[2m               \u001b[0m\n",
       "         TrainingManager                                                                            \u001b[2m               \u001b[0m\n"
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     },
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
     ]
    },
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       "Output()"
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      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">INFO    </span> Computing metrics on test subset <span style=\"color: #808000; text-decoration-color: #808000\">...</span>                                              <a href=\"file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">luxonis_lightning.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py#816\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">816</span></a>\n",
       "</pre>\n"
      ],
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       "\u001b[32mINFO    \u001b[0m Computing metrics on test subset \u001b[33m...\u001b[0m                                              \u001b]8;id=697473;file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py\u001b\\\u001b[2mluxonis_lightning.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=563380;file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py#816\u001b\\\u001b[2m816\u001b[0m\u001b]8;;\u001b\\\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">INFO    </span> Metrics computed.                                                                 <a href=\"file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">luxonis_lightning.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py#818\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">818</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[32mINFO    \u001b[0m Metrics computed.                                                                 \u001b]8;id=805636;file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py\u001b\\\u001b[2mluxonis_lightning.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=662174;file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py#818\u001b\\\u001b[2m818\u001b[0m\u001b]8;;\u001b\\\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">INFO    </span> Test loss: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7.9920</span>                                                                <a href=\"file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">luxonis_lightning.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py#1041\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1041</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[32mINFO    \u001b[0m Test loss: \u001b[1;36m7.9920\u001b[0m                                                                \u001b]8;id=969654;file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py\u001b\\\u001b[2mluxonis_lightning.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=286649;file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py#1041\u001b\\\u001b[2m1041\u001b[0m\u001b]8;;\u001b\\\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">────────────────────────────────────────────────────── </span>Test<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> ───────────────────────────────────────────────────────</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;35m────────────────────────────────────────────────────── \u001b[0mTest\u001b[1;35m ───────────────────────────────────────────────────────\u001b[0m\n"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Loss:</span> <span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0; font-weight: bold\">7.991967439651489</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;35mLoss:\u001b[0m \u001b[1;37m7.991967439651489\u001b[0m\n"
      ]
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    },
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     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">Metrics:</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1;35mMetrics:\u001b[0m\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-style: italic\">         EfficientBBoxHead-boundingbox         </span>\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┓\n",
       "┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Name                             </span>┃<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\"> Value    </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━┩\n",
       "│<span style=\"color: #800080; text-decoration-color: #800080\"> MeanAveragePrecision-boundingbox </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> 0.75609  </span>│\n",
       "│<span style=\"color: #800080; text-decoration-color: #800080\"> map_50                           </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> 0.95050  </span>│\n",
       "│<span style=\"color: #800080; text-decoration-color: #800080\"> map_75                           </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> 0.88366  </span>│\n",
       "│<span style=\"color: #800080; text-decoration-color: #800080\"> map_small                        </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> -1.00000 </span>│\n",
       "│<span style=\"color: #800080; text-decoration-color: #800080\"> map_medium                       </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> -1.00000 </span>│\n",
       "│<span style=\"color: #800080; text-decoration-color: #800080\"> map_large                        </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> 0.75609  </span>│\n",
       "│<span style=\"color: #800080; text-decoration-color: #800080\"> mar_1                            </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> 0.71061  </span>│\n",
       "│<span style=\"color: #800080; text-decoration-color: #800080\"> mar_10                           </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> 0.79242  </span>│\n",
       "│<span style=\"color: #800080; text-decoration-color: #800080\"> mar_100                          </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> 0.79242  </span>│\n",
       "│<span style=\"color: #800080; text-decoration-color: #800080\"> mar_small                        </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> -1.00000 </span>│\n",
       "│<span style=\"color: #800080; text-decoration-color: #800080\"> mar_medium                       </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> -1.00000 </span>│\n",
       "│<span style=\"color: #800080; text-decoration-color: #800080\"> mar_large                        </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> 0.79242  </span>│\n",
       "│<span style=\"color: #800080; text-decoration-color: #800080\"> f1_small                         </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> -1.00000 </span>│\n",
       "│<span style=\"color: #800080; text-decoration-color: #800080\"> f1_medium                        </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> -1.00000 </span>│\n",
       "│<span style=\"color: #800080; text-decoration-color: #800080\"> f1_large                         </span>│<span style=\"color: #c0c0c0; text-decoration-color: #c0c0c0\"> 0.77383  </span>│\n",
       "└──────────────────────────────────┴──────────┘\n",
       "</pre>\n"
      ],
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       "\u001b[3m         EfficientBBoxHead-boundingbox         \u001b[0m\n",
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┓\n",
       "┃\u001b[1;35m \u001b[0m\u001b[1;35mName                            \u001b[0m\u001b[1;35m \u001b[0m┃\u001b[1;35m \u001b[0m\u001b[1;35mValue   \u001b[0m\u001b[1;35m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━┩\n",
       "│\u001b[35m \u001b[0m\u001b[35mMeanAveragePrecision-boundingbox\u001b[0m\u001b[35m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m0.75609 \u001b[0m\u001b[37m \u001b[0m│\n",
       "│\u001b[35m \u001b[0m\u001b[35mmap_50                          \u001b[0m\u001b[35m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m0.95050 \u001b[0m\u001b[37m \u001b[0m│\n",
       "│\u001b[35m \u001b[0m\u001b[35mmap_75                          \u001b[0m\u001b[35m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m0.88366 \u001b[0m\u001b[37m \u001b[0m│\n",
       "│\u001b[35m \u001b[0m\u001b[35mmap_small                       \u001b[0m\u001b[35m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m-1.00000\u001b[0m\u001b[37m \u001b[0m│\n",
       "│\u001b[35m \u001b[0m\u001b[35mmap_medium                      \u001b[0m\u001b[35m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m-1.00000\u001b[0m\u001b[37m \u001b[0m│\n",
       "│\u001b[35m \u001b[0m\u001b[35mmap_large                       \u001b[0m\u001b[35m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m0.75609 \u001b[0m\u001b[37m \u001b[0m│\n",
       "│\u001b[35m \u001b[0m\u001b[35mmar_1                           \u001b[0m\u001b[35m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m0.71061 \u001b[0m\u001b[37m \u001b[0m│\n",
       "│\u001b[35m \u001b[0m\u001b[35mmar_10                          \u001b[0m\u001b[35m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m0.79242 \u001b[0m\u001b[37m \u001b[0m│\n",
       "│\u001b[35m \u001b[0m\u001b[35mmar_100                         \u001b[0m\u001b[35m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m0.79242 \u001b[0m\u001b[37m \u001b[0m│\n",
       "│\u001b[35m \u001b[0m\u001b[35mmar_small                       \u001b[0m\u001b[35m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m-1.00000\u001b[0m\u001b[37m \u001b[0m│\n",
       "│\u001b[35m \u001b[0m\u001b[35mmar_medium                      \u001b[0m\u001b[35m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m-1.00000\u001b[0m\u001b[37m \u001b[0m│\n",
       "│\u001b[35m \u001b[0m\u001b[35mmar_large                       \u001b[0m\u001b[35m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m0.79242 \u001b[0m\u001b[37m \u001b[0m│\n",
       "│\u001b[35m \u001b[0m\u001b[35mf1_small                        \u001b[0m\u001b[35m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m-1.00000\u001b[0m\u001b[37m \u001b[0m│\n",
       "│\u001b[35m \u001b[0m\u001b[35mf1_medium                       \u001b[0m\u001b[35m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m-1.00000\u001b[0m\u001b[37m \u001b[0m│\n",
       "│\u001b[35m \u001b[0m\u001b[35mf1_large                        \u001b[0m\u001b[35m \u001b[0m│\u001b[37m \u001b[0m\u001b[37m0.77383 \u001b[0m\u001b[37m \u001b[0m│\n",
       "└──────────────────────────────────┴──────────┘\n"
      ]
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">───────────────────────────────────────────────────────────────────────────────────────────────────────────────────</span>\n",
       "</pre>\n"
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       "\u001b[1;35m───────────────────────────────────────────────────────────────────────────────────────────────────────────────────\u001b[0m\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">INFO    </span> Test main metric                                                                 <a href=\"file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">luxonis_lightning.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py#1050\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">1050</span></a>\n",
       "         <span style=\"font-weight: bold\">(</span>EfficientBBoxHead-boundingbox/MeanAveragePrecision-boundingbox<span style=\"font-weight: bold\">)</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7561</span>         <span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">                         </span>\n",
       "</pre>\n"
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       "\u001b[32mINFO    \u001b[0m Test main metric                                                                 \u001b]8;id=335715;file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py\u001b\\\u001b[2mluxonis_lightning.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=30238;file:///opt/conda/lib/python3.11/site-packages/luxonis_train/models/luxonis_lightning.py#1050\u001b\\\u001b[2m1050\u001b[0m\u001b]8;;\u001b\\\n",
       "         \u001b[1m(\u001b[0mEfficientBBoxHead-boundingbox/MeanAveragePrecision-boundingbox\u001b[1m)\u001b[0m: \u001b[1;36m0.7561\u001b[0m         \u001b[2m                         \u001b[0m\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">                      Test metric                       </span>┃<span style=\"font-weight: bold\">                      DataLoader 0                      </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">                       test/loss                        </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                    7.99196720123291                    </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> test/loss/EfficientBBoxHead-boundingbox/AdaptiveDetec… </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                    7.99196720123291                    </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> test/loss/EfficientBBoxHead-boundingbox/AdaptiveDetec… </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                    0.74022376537323                    </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> test/loss/EfficientBBoxHead-boundingbox/AdaptiveDetec… </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                  0.10350887477397919                   </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\"> test/metric/EfficientBBoxHead-boundingbox/MeanAverage… </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                   0.7560890913009644                   </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">   test/metric/EfficientBBoxHead-boundingbox/f1_large   </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                   0.7738303542137146                   </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">  test/metric/EfficientBBoxHead-boundingbox/f1_medium   </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                          -1.0                          </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">   test/metric/EfficientBBoxHead-boundingbox/f1_small   </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                          -1.0                          </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">    test/metric/EfficientBBoxHead-boundingbox/map_50    </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                   0.9504950642585754                   </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">    test/metric/EfficientBBoxHead-boundingbox/map_75    </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                   0.8836633563041687                   </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">  test/metric/EfficientBBoxHead-boundingbox/map_large   </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                   0.7560890913009644                   </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">  test/metric/EfficientBBoxHead-boundingbox/map_medium  </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                          -1.0                          </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">  test/metric/EfficientBBoxHead-boundingbox/map_small   </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                          -1.0                          </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">    test/metric/EfficientBBoxHead-boundingbox/mar_1     </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                   0.710606038570404                    </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">    test/metric/EfficientBBoxHead-boundingbox/mar_10    </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                   0.7924242615699768                   </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">   test/metric/EfficientBBoxHead-boundingbox/mar_100    </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                   0.7924242615699768                   </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">  test/metric/EfficientBBoxHead-boundingbox/mar_large   </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                   0.7924242615699768                   </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">  test/metric/EfficientBBoxHead-boundingbox/mar_medium  </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                          -1.0                          </span>│\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">  test/metric/EfficientBBoxHead-boundingbox/mar_small   </span>│<span style=\"color: #800080; text-decoration-color: #800080\">                          -1.0                          </span>│\n",
       "└────────────────────────────────────────────────────────┴────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
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       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m                     Test metric                      \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m                     DataLoader 0                     \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m                      test/loss                       \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                   7.99196720123291                   \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36mtest/loss/EfficientBBoxHead-boundingbox/AdaptiveDetec…\u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                   7.99196720123291                   \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36mtest/loss/EfficientBBoxHead-boundingbox/AdaptiveDetec…\u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                   0.74022376537323                   \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36mtest/loss/EfficientBBoxHead-boundingbox/AdaptiveDetec…\u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                 0.10350887477397919                  \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36mtest/metric/EfficientBBoxHead-boundingbox/MeanAverage…\u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                  0.7560890913009644                  \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m  test/metric/EfficientBBoxHead-boundingbox/f1_large  \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                  0.7738303542137146                  \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m test/metric/EfficientBBoxHead-boundingbox/f1_medium  \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                         -1.0                         \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m  test/metric/EfficientBBoxHead-boundingbox/f1_small  \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                         -1.0                         \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m   test/metric/EfficientBBoxHead-boundingbox/map_50   \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                  0.9504950642585754                  \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m   test/metric/EfficientBBoxHead-boundingbox/map_75   \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                  0.8836633563041687                  \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m test/metric/EfficientBBoxHead-boundingbox/map_large  \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                  0.7560890913009644                  \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m test/metric/EfficientBBoxHead-boundingbox/map_medium \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                         -1.0                         \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m test/metric/EfficientBBoxHead-boundingbox/map_small  \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                         -1.0                         \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m   test/metric/EfficientBBoxHead-boundingbox/mar_1    \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                  0.710606038570404                   \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m   test/metric/EfficientBBoxHead-boundingbox/mar_10   \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                  0.7924242615699768                  \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m  test/metric/EfficientBBoxHead-boundingbox/mar_100   \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                  0.7924242615699768                  \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m test/metric/EfficientBBoxHead-boundingbox/mar_large  \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                  0.7924242615699768                  \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m test/metric/EfficientBBoxHead-boundingbox/mar_medium \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                         -1.0                         \u001b[0m\u001b[35m \u001b[0m│\n",
       "│\u001b[36m \u001b[0m\u001b[36m test/metric/EfficientBBoxHead-boundingbox/mar_small  \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m                         -1.0                         \u001b[0m\u001b[35m \u001b[0m│\n",
       "└────────────────────────────────────────────────────────┴────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weights = luxonis_model.get_min_loss_checkpoint_path() # gets checkpoint where validation loss was the lowest\n",
    "# weights = luxonis_model.get_best_metric_checkpoint_path() # gets checkpoint where validation metric was the highest\n",
    "\n",
    "metrics = luxonis_model.test(view=\"test\", weights=weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name =\"infer\"></a>\n",
    "\n",
    "### 🧠 Infer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usually, we also want to visualize the prediction of the trained model on test images to ensure it does what it is supposed to do. This is called inference, and we can perform it either on one of the views (e.g., test) or a random image, directory of images, or whole video (for more details, refer to the [docs](https://github.com/luxonis/luxonis-train/tree/main?tab=readme-ov-file#inference)). In our case, we'll infer the model on test images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NOTE: If you are using Google Colab use this and images will be saved to \"infer_results_cats_dogs_model\" directory\n",
    "luxonis_model.infer(weights=weights, save_dir=\"infer_results_cats_dogs_model\", view=\"test\")\n",
    "\n",
    "# NOTE: If you are not using Google Colab use this and images will be displayed\n",
    "# luxonis_model.infer(\n",
    "#     weights=weights,\n",
    "#     view=\"test\"\n",
    "# ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's visualize one of the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1Se54VddVGlRVPaiHw6oaVnVVFsWpU/NLFy7cdMenzyydpZgiK7CI2crqBhPra8uceEXr/+AsJ14zK+97ngmr5MhJyZSC/OyX7zPxXXd8+08+9yVvnX767fOP/O7eZzzVP2shvP3MkzfP97/p9/76O7/wF1OW/aP/9/73f/VvTy/Md+bnO/Oz7U4nb7WyPEdizDDDIgTx6nMQ1xOSoSAZEy7kw16vu7N7sLOzv711sLfb3d9P1TCPMYsWDNXVoN8fDAb1cFBVw2E1zLM4Mz29dNOFW97ymfMr5yjb5OXKfVtMrK8tc+KGESOS3H1ne/fpJ0eeevLpp/b29kOwPM+bzWa73e7MzJw6tbi0tDQ/P9eeajeajbIoYhZDtBFAwiU5FgwIhhk3iHhN1QbyC/deZuLiDw4e+oNHv/2D87uHHnzwjpUPVjb9ExtfeuyNL1h48EvPP/SLlz/v559+66fPX/7Gz/4NK5uNdmd6Yb4zP9+Zn213OnmrleU5EmOGGRYhiFefg7iekAwFyZhwIR/2et2d3YOdnf3trYO93e7+fqqGeYxZtGCorgb9/mAwqIeDqhoOq2GexZnp6aWbLtzyls+cXzlH2SYvsYjZyuoGE+try5x4RSurG5x4bXxo6a6V9z3PhFVy3EmVeQpKZ7+yy8T3veU7/+dH/uF3fNp72xmeGNAoivy39z/9UjX/79c//QNf8vMU5Tv+3V99/1f/ZtZoNKanO3NznfnZ6fn5RrMJBshFCGYZFsRrwkFcIyc5eMQMCOBOXfcOD7cvX96+vHnl0qWjw30fDkF5DHmMjSKaVA0H1XBYVcPBce/ouCv5VKuxdNPNt9919+KFWyinyMuV+64wsb62zImXt7K6wYkbYv2Dy0Bt4H7hvstMXHzP8T95V+/TGo/dln79dy/dfildiGaf23n4XGv7Wx/7+z/4th+jLN/xW3/3/V/8CxSl8tIajazRaExPd+bmOvOz0/PzjWYTDJCLEMwyLIjXhIO4Rk5y8IgZEMCduu4dHm5fvrx9efPKpUtHh/s+HILyGPIYG0U0qRoOquGwqoaD497RcVfyqVZj6aabb7/r7sULt1BOkZdYxMLK6gYT62vLnHhFK6sbnHhtfGjprpX3Pc+EVXI8mdekOng6+zeOmPj+z/jub3vwH37P7e9uRSm5Wfju577qnvknP3Nx/Rv/8G/+2F/6OcrG1/w/f+1HvvxXPY952WzNtDsL8wunT093OiHPYwhyCMEsw4IA4YwFwHgp4QgIZvy3cBBCI27uQARjLNVV6vUOd/e2Ll/a3ry8u7XVOzoK7hFlwTKzoogG8uR1lYbVYNDrdg/rOuV5XLpw4c33fO7izW+gOU3RWLnvChPra8uceHkrqxucuCHWP7gM1AaeLty3ycTFdx993rfk3/v5v7i//kx9dFxk7TLab+/csVnN/4edt33gbT9IUbzjP7/jR//iz1M2VBQpFp7HvGy2ZtqdhfmF06enO52Q5zEEOYRglmFBgHDGAmC8lHAEBDP+WzgIoRE3dyCCMZbqKvV6h7t7W5cvbW9e3t3a6h0dBfeIsmCZWVFEA3nyukrDajDodbuHdZ3yPC5duPDmez538eY30JymaGARCyurG0ysry1z4hWtrG5w4rXxoaW7ln/o42YGWKVk8pASqi3VK195xMS73/pd733i3s+defBzOg//2u5nblULv3bw5773jh85irPf/MDf+tEv/ABZ+bX/31f+0OqveBbIi7xRTs/NnDpzZm5+YarTycsSDAsQRQBcyAVYsGBgGGMChEtyATHaCNeRGDHjJcQLktxTjRTMDAsBc5APj44Pd3f2t7d3trYO9ncHR0fVoE+dgpKnFDyBIoQA7vJUV8P+oJeq2uVnVs7eefc9Z265jfYs5dTK/VeYWF9b5sTLW1nd4MQN8fy/XTKz2mTy8/duMnHxPUef+03Fu77gF/eff2rYPZzK8nc9/7feNvXwZ7Q+9i+e/Uc/8uZ3hbLxtR/+uvd9/v9NVtIoPc9TjORF3iin52ZOnTkzN78w1enkZQmGBYgiAC7kAixYMDCMMQHCJbmAGG2E60iMmPES4gVJ7qlGCmaGhYA5yIdHx4e7O/vb2ztbWwf7u4Ojo2rQp05ByVMKnkARQgB3eaqrYX/QS1Xt8jMrZ++8+54zt9xGe5ZyihixsLK6wcT62jInXtHK6gYT62vL/OmIMdE77m1f2X76yac/8tEPP/PMM7u7u4PBMBh5XpTNMstyM8qybE9Pz88vnFlaPnVqYX5hfn5hfmFurtFquiQHBFgwgdcJCFmMwZAAM+OPI4n/GpcAuQALBgQbAyQ8CQjBzPgkIRdgwTAQciU5YBZCMDPGxJhx1cq9G0x8aOmu0+95NsZgI5VqkwLJUiLVK1/RZeLdb/nO9d3832x/GVjL+v/gzC/8+4PP+4Pum26e2n7g8Pw77/lpK8L3PfCXCPZP3/77yjKyfKrTPnV6ce7U4uzcfKM9ZSFiEQIECQe5AAOCBcOMMSHhLiEgRhvhOhIjZryEuEo+ViOZhRjMMHlKg+HxwcH25cv7V7b2drb73W49rMxTZqCkuk51pZQMzyyA8LquRgZVVXlKi2eW77z77jO33MbMAuXUyv1XmFhfW+bEy1tZ3WBifW2ZPyMxJg4PDi+trz/x2BMf+chHPv78x/u9fu3JLOR51mw0QozuHkIoG412uz23ML+4uHjmzNLS8pml5aV2uw1IjJhZjOYi1ckhy2I0A3EdM+PFJHGNS0Aw4zouAXIxYcGCjQGSPDESghkIuXO9EMwMhFxJjsDGQjAzECNizECuJL/wZZtMPPvzpzHIzKTz924y8eh7j/7ed9efNfvQG6vf+PX12/fTwm8evu07z7+z61Pf9tw//tE3f2/WaL7jD/+HH3r7j3uWhUbT89xjVJaR5VOd9qnTi3OnFmfn5hvtKQsRixAgSDjIBRgQLBhmjAkJdwkBMdoI15EYMeMlxFXysRrJLMRghslTGgyPDw62L1/ev7K1t7Pd73brYWWeMgMl1XWqK6VkeGYBhNd1NTKoqspTWjyzfOfdd5+55TZmFiiniBELK6sbTKyvLXPiFa2sbjCxvrbMn44YE4cHh5fW15947ImPfOQjH3/+4/1ev/ZkFvI8azYaIUZ3DyGUjUa73Z5bmF9cXDxzZmlp+czS8lK73QYkRswsRnOR6uSQZTGagbiOmfFikrjGJSCYcR2XALmYsGDBxgBJnhgJwQyE3LleCGYGQq4kR2BjIZgZiBExZrBy3wYTH1q6a/Hdz5gZhlWqDQW5pYTXK19+yMT3v/k76sODZpaXMUYIIWZ5HorCyobyvMI85tZsUpSexRQjIZat5tzC/Nz8/NzCqVZnOuSFhQwLEMAEngARGAmYGRgIhJC7CAQzwzDGxIgQYBgjxpgYM0AjIJeDAmYYqB4OB93u4c7O1uXNvStbB3u7w+Njr6vcwnSrmcfgdZXqSlWFHIOUPNV1NegPB3VVq04Ly0t3ftY9Z265jc4c5dTK/VeYWF9b5sTLW1ndYGJ9bZk/IzEm9nf3nnnq6UcvPvrRj350Y2O9rhNGFrOy0Wg0GzGEqq4lQrCiKMpWc2Hh1IUL50fOnj/b6cyEECyEGIJZiNGAJAFmBkLiOsGMF3OJ6wQb4zqSuMalYMb1BJiBXEkuCYFhFswsBMyMESHkLrkwogWLBki4CwgYYzp7/yUmnvyZebnyqcLQ+dVNJi6+9/gPf/dj/9vameFgUPrR31/6hV89ePsfHL7pQmPzoeNb3vvWf1008m+/+Fcx/vln/Ko1mxSlZzHFSIhlqzm3MD83Pz+3cKrVmQ55YSHDAgQwgSdABEYCZgYGAiHkLgLBzDCMMTEiBBjGiDEmxgzQCMjloIAZBqqHw0G3e7izs3V5c+/K1sHe7vD42OsqtzDdauYxeF2lulJVIccgJU91XQ36w0Fd1arTwvLSnZ91z5lbbqMzRzlFjFhYWd1gYn1tmROvaGV1g4n1tWWuc+kb7lh610X+JMSY2N/de+appx+9+OhHP/rRjY31uk4YWczKRqPRbMQQqrqWCMGKoihbzYWFUxcunB85e/5spzMTQrAQYghmIUYDkgSYGQiJ6wQzXswlrhNsjOtI4hqXghnXE2AGciW5JASGWTCzEDAzRoSQu+TCiBYsGiDhLiBgZ+/fYOJDS3edfs+zdV3LZbVqUJBMiVStfNkhE++68ztS92CqKBoxj4EsZnlRhLywspFiVkHKc8omRakseoi1kzeK1nR7dnZ2YfFUuzOTN5qxKC1mWMAMmQQIBBgGhokRGUgIMDMwjDEBEmPGiIEYM8YEGgGZiTFTknsaHh8f7+3vb29tb20d7O4cHXarfj8oFVnWmZoqs+h1lera60qeTJ5S8uGwqgaD4aCqKrmfWlq58+57Fm+5lfYsZWvl/itMrK8tc+LlraxuMLG+tsyfkRgT21tXHr/4+CMPPfTAAw9sbl5OkMVYFEXZaDSazRhDXdUpJUchxCzPZuZmz02snDt7an6h1WoVjTLPooXAhBgzMxAS1wlmvJhLXCfYGC9DEi9mGCNCruQuF2AGIZhZCGbGJygpySUZuHtVVXVdD4c1EkaMMcvyO/7OIRNP/syC3IupAnR+dZOJi+89Xn/koc0nHt985tnUPZgqikbMYyCLWV4UIS+sbKSYVZDynLJJUSqLHmLt5I2iNd2enZ1dWDzV7szkjWYsSosZFjBDJgECAYaBYWJEBhICzAwMY0yAxJgxYiDGjDGBRkBmYsyU5J6Gx8fHe/v721vbW1sHuztHh92q3w9KRZZ1pqbKLHpdpbr2upInk6eUfDisqsFgOKiqSu6nllbuvPuexVtupT1L2SJGLKysbjCxvrbMiVe0srrBxPraMte59A13LL3rIi/2Az/b/fqvaPMSYkxsb115/OLjjzz00AMPPLC5eTlBFmNRFGWj0Wg2Ywx1VaeUHIUQszybmZs9N7Fy7uyp+YVWq1U0yjyLFgITYszMQEhcJ5jxYi5xnWBjvAxJvJhhjAi5krtcgBmEYGYhmBmfoKQkl2Tg7lVV1XU9HNZIGJ/xtRUTH1q668x7n6uqWu7mqkEmoYTXK/cfMPGuN39H6nY7jbKR51kMeTZSxLK0okwxq6COuTWaynPFWEFVJyzmzaIzPbOwMD8zN9+cbhfNqVAWFjPMwJCBQIzIACHAMEZMjBkYJsSYwMSIjBETIzLGJIQ7iGAjiFTXaTDoHR4e7Ozu7VzZ39457h5W/b5SyiHPY7PIMzOl5HWdUq2UpKS6robDajAY1oNU1+5aPHv2TXd/zuItt9CaJm+u3H+FifW1ZU68vJXVDSbW15b54/zAz3a//ivavByBwLl86fIDH/kvDz3wwCOPXNza2oojeVYURdlotFrNkGWeUp2SXARilk21pxYWTi2eXjwzsrS0vLQ00+nkZU4wHJcAMyxYDIbEdcyMF5PEdcyMP44kwCUgmJkMR+AuRoxgTJgBgevJlVIaDKv+8fHB4WG3e9w7PhpWNdAoi05n5gu/ZYqJ537+jFDMDXR2dZOJiz/YX3/4wc0nHtt89tnU7XYaZSPPsxjybKSIZWlFmWJWQR1zazSV54qxgqpOWMybRWd6ZmFhfmZuvjndLppToSwsZpiBIQOBGJEBQoBhjJgYMzBMiDGBiREZIyZGZIxJCHcQwUYQqa7TYNA7PDzY2d3bubK/vXPcPaz6faWUQ57HZpFnZkrJ6zqlWilJSXVdDYfVYDCsB6mu3bV49uyb7v6cxVtuoTVN3iRGLKysbjCxvrbMiVe0srrBxPrash9s7X3gm/1oPzt9U//Dv7L0rou+v7n7o9+owZGVU3Nf86/e9HXpdz7/W/1oP546P3jwPy1934OMCATO5UuXH/jIf3nogQceeeTi1tZWHMmzoijKRqPVaoYs85TqlOQiELNsqj21sHBq8fTimZGlpeWlpZlOJy9zguG4BJhhwWIwJK5jZrouQcIAACAASURBVLyYJK5jZvxxJAEuAcHMZDgCdzFiBGPCDAhcT66U0mBY9Y+PDw4Pu93j3vHRsKqBv/E9NzPxoaW7lt/7cZeEzFWDTEIJTyv37zPx/W/5Tj/qTjebjTzPQshizIqcrCAvyPI6RIoiNqasKBTjQBoMh7Vj0VrNqbn52ZnO3PTsTNluZ61WlueEgBkExsSIGBEOGIERY8IYEwgZIyZGZIyYGJExJlzCAQuGI3k1GPSPjo73D/Z2rnT39roH3brfw1Mwy2PIzQIEOe5KI7U8udc+rIbDYTUYDKtBSrVLi2fPvemez168+Q002+SNlfuvMLG+tsyJl7eyusHE+toyf5w7vurSxZ9a4uUIBM7zzz3/B7/3+w898ODjjz+xu7ubxRjzkaxsNBrNZp5nmElyiWAxi0VZtlpT09PTc3NzyyvLb3jDradPn2pNNUMW69olRmKwEC0GM159kgCXgIAZhjMigWFMBMacq8SIUkqDqhoc9Q663YP9g/29vf2Dg97RcVVXQFE25mZn/s6738TEsz93OsuigoPOrm4ycfGH+pcefvDSE49vPfusH3Wnm81GnmchZDFmRU5WkBdkeR0iRREbU1YUinEgDYbD2rForebU3PzsTGduenambLezVivLc0LADAJjYkSMCAeMwIgxYYwJhIwREyMyRkyMyBgTLuGABcORvBoM+kdHx/sHeztXunt73YNu3e/hKZjlMeRmAYIcd6WRWp7cax9Ww+GwGgyG1SCl2qXFs+fedM9nL978Bppt8gYxYmFldYOJ9bVlTryildUNJtbXlp9877f8Lx/7u4f5Uuxt/0+9r8q+7ff++b/8cDfO/637lv7O6V/+np/T+x57++fccvAz77yj/+Ff2X3fP1x+71OMCATO8889/we/9/sPPfDg448/sbu7m8UY85GsbDQazWaeZ5hJcolgMYtFWbZaU9PT03Nzc8sry294w62nT59qTTVDFuvaJUZisBAtBjNefZIAl4CAGYYzIoFhTATGnKvEiFJKg6oaHPUOut2D/YP9vb39g4Pe0XFVV8A3/ewXMvGhpbvm/tUTeaPIsmi1qgDITY6nlfv3mXj3Z353fXTUaTSKLMvMLISYRY/RQyQrQ6NZtNrFdCdvNIlx6H7UHwzrSlJeFO2x6Xan05qebnY6eaNhMWCREMAwMSIDgRhxI4AxJibEmIFhYkTGiAkJDIcgEM5V8pSqanB8dLi/f7i3f7C32+t2q/6AlPIsllkss8Lkw/5AdWUSSkrJvfZUV8PhsD8YDvqDalCnWtLiyrk777ln8eZbmZqmaKzct8XE+toyJ17eyuoGE+tryzsH/s0/sL936HlmP/BNs9v7/j/+wP7BkX/1X2593X1T3/MTh+/8me7b31r+1P86z3+VIEHSx5997nd++3ceevDBp5955vDgMGZxxLC8zJvNZlGWWZ7HGAmWxRjzPIRAsJhlZVmeWVq64/bbz50/Nz8/W5ZlldxdIYQshhAtGMafhiRezMy4jiRGhLnxEoEXcQTukvtxr7e/t7ezs7u9dWVnb29/b6/X66VUJx/Ls7zdnvqXv/RXmPjYT842W80UUoCzq5tMPPpD/UuPPHzpY09cee6Z+uio02gUWZaZWQgxix6jh0hWhkazaLWL6U7eaBLj0P2oPxjWlaS8KNpj0+1OpzU93ex08kbDYsAiIYBhYkQGAjHiRgBjTEyIMQPDxIiMERMSGA5BIJyr5ClV1eD46HB//3Bv/2Bvt9ftVv0BKeVZLLNYZoXJh/2B6soklJSSe+2probDYX8wHPQH1aBOtaTFlXN33nPP4s23MjVN0cAiZiurG0ysry1z4hWtrG4wsb62/HV/+yf+yt+/9/4vmv7p/9D9rf/rx1uf/99/wUNf/9n/+4/8hX+694f/em7zWz/78x/7mUd/7oJlBV5v/JM7lt/9BCOCBEkff/a53/nt33nowQeffuaZw4PDmMURw/IybzabRVlmeR5jJFgWY8zzEALBYpaVZXlmaemO228/d/7c/PxsWZZVcneFELIYQrRgGH8akngxM+M6khgR5sZLBF7EEbhL7se93v7e3s7O7vbWlZ29vf29vV6vl1KdfOx7f+urmfjQ0l3+L/7zwuJCs9W0WlUA5CbH08r9+0y8523fW3W77aIoYkACLITaQsJC0WzNzDRn5tpzc0WzbVmskh/3B/1qWNXJgjXKRrPRbLXbrel2a3a2bDYti5bFGDJCsAAYGGPOiBsjgTFnQgTAwEAYyBgxIXAmxAuEVA+HVb93fHi4v7t3uL/f3d8f9nuqUxatkedlUZR5EZxBr+d1FRByT0mpruvhYKQ30h8O+3Vdufvi8tk777n79M23WmeGsrly3xYT62vLnHh5K6sbTKyvLf/T79v7oreVf/3PN3/6Px5/+LEKuP8LmrdfyL7o67c+/GNngDu+6tLFn1ri5QiSqPXs08/+5m/85sMPPfjx9fXjo+MsiyNAzLJms1k2GnlZjMQYQwiEgFHLkTCbX1i47dY33nTzhaWl0+12G4wQ8iwGMwJmBIwJM16BJCbMDJDkEhPBDDAzXkwSwty4KvACZyxwlVyp9mpYDYfD/f39S5cubW5ubl25crB/0Ov3Ul1jhlSnFEIoi+L//N2/zcTFD0x1ZjoppABnVzeZePR9g0uPXLz81ONXnnu26nbbRVHEgARYCLWFhIWi2ZqZac7MtefmimbbslglP+4P+tWwqpMFa5SNZqPZardb0+3W7GzZbFoWLYsxZIRgATAwxpwRN0YCY86ECICBgTCQMWJC4EyIFwipHg6rfu/48HB/d+9wf7+7vz/s91SnLFojz8uiKPMiOINez+sqIOSeklJd18PBSG+kPxz267py98Xls3fec/fpm2+1zgxlE4uYraxuMLG+tsyJV7SyusHE+tryZ97/4O/95KcVjaIeVk99w13L3/eRH/+Gf7H9Od/yo7/cf/wn5ze/9bM//7GfefTnLlhWqK4ufcMdy+/5GCOCJGo9+/Szv/kbv/nwQw9+fH39+Og4y+IIELOs2WyWjUZeFiMxxhACIWDUciTM5hcWbrv1jTfdfGFp6XS73QYjhDyLwYyAGQFjwoxXIIkJMwMkucREMAPMjBeThDA3rgq8wBkLXCVXqr0aVsPhcH9//9KlS5ubm1tXrhzsH/T6vVTXmCHVKf3QR76WiQ8t3bX39f/x3IWznZmOuWqQSSjhaeX+fSbe81nvrA4PyixESHXylMBqQmU0Wu2ZU4szpxZnF06X022LWXId94eD4bCqK5JiFrM8y8tGo92empkpWo2QZ1k+UliWhRDMjBAYcYHAGAnggPgkAyOIMWNMjDgTYsyQU1dVv3/U7R7u7+/t7h0d7PeOjtKwDngeY6MoiiwviyICKSEPgMs9VfWwGg4HvV736Kh3fNTr9arhICVfXF56812ftXTrrWF2wZqtlfu2mFhfW+bEy1tZ3WBifW35z33N5n/+kcU8s+Qc9RQC//bXe09tpB/9paMn/s0ScMdXXbr4U0u8HEEStZ5+8plf/7Vfe+Thh7e2Nvu9fszzGKMZMSuajaJoNMpGmRVFnmUuVXXlELIIVFXVak2dO3d2ZeXsuQtnFxYWWq2poiyyEAjIBRhmgZFgvAKXmAg2BkjiOmbGHyVIvCAw5ogxAwIjdZ0Gx4Pj4+PDw8PNra3nnntua2ure3jYHwzcPcaY53kwq6oquccQ3v0H72Di4fc3Z2dnlTno7OomE4/+8HDzsYuXnvzY1rPPVocHZRYipDp5SmA1oTIarfbMqcWZU4uzC6fL6bbFLLmO+8PBcFjVFUkxi1me5WWj0W5PzcwUrUbIsywfKSzLQghmRgiMuEBgjARwQHySgRHEmDEmRpwJMWbIqauq3z/qdg/39/d2944O9ntHR2lYBzyPsVEURZaXRRGBlJAHwOWeqnpYDYeDXq97dNQ7Pur1etVwkJIvLi+9+a7PWrr11jC7YM0WFjFbWd1gYn1tmROvaGV1g4n1teW3fsXjv/HNj87c89d6v//Bvff/s29c/NAX+S/8xT9/5ot/+K6PfuNv9x/4T/f80jd+9NsebN612vv9D+69/58tv+dJRgRJ1Hr6yWd+/dd+7ZGHH97a2uz3+jH//9mD0xhLs/M+7P/nec457/vepW4tXdVV1d1DjrhoIamFMxYCw4kSJ1ACWM3RMIgjGXHiBFaCGNYHW7aRfFEWKBCMCBJkRbBELRQtyZYUxRLVEJCQnBkJJi1FGMvhkOwh2dOzd1V3V1d1Vd173+2c8zy59w5HnDE5I2qBP83v50WECOJCVYZQlkVZuBC8c2oWU1SAnQCIMQ4Gw8uXL+3vX7r8wKWtra3BYBiK4JjBMDUABCLGAhPehJphhWkJgJnhNYgIX8mAjC9hLCkMSwSAsZBS7uquruvpdHr36Oill146OjqaTadt16mqiHjvmSjG+JP/6r/GypO7D9/5b3/7wQffvr6+TmoJMDKFKTTvP3qGlZ/6d36iOT0NDOTUd33OWY0gAl8O18YbO7sbF7bHm1thMDSSlHPXx65Pse9NlQjOeQmhHFTleOwHJYXggytCId47ceSEiUEEGGHJQIDBDGZqAIEBEGOBQEQAYcmwYFgyA2xBU7a+75p6Np2en5+dnZ41s1lfNzknR+TZlcEHH7wTzyIEIhJAYZZyin3ftU3TTKezeT1r6rrr2pzShd3db3nood13vFO2tmkw3H/kCCsH1/bwlje2f/UQKwfX9r71v7zzBz+3Ezxh5a/9Tyd/5S+Wf/mh4jv+1tEXf20XwDd8z+3P/8ou3ogB2Szqczefe+LxJz5//frJyf2ua13wzgfvJIRQlqUvCh+C845FNOemaTLMFwFEXd8XIWxtbu3sXrx85fL2zsWNyWQwGhTBM7HBABCIGAtMeBNqhhWmJayYGV5FRPhKBmR8CWNJYVgiwGBZtanb87Oz0/unR/fu3blz9+Dg1tnpaeyjmoqIc86HACDGqDmr2c9/7r/Hyqd/2m1sbUpBgF26ehcrN3423rnxxbvP3rz70gvN6WlgIKe+63POagQR+HK4Nt7Y2d24sD3e3AqDoZGknLs+dn2KfW+qRHDOSwjloCrHYz8oKQQfXBEK8d6JIydMDCLACEsGAgxmMFMDCAyAGAsEIgIIS4YFw5IZYAuasvV919Sz6fT8/Ozs9KyZzfq6yTk5Is+uDD744J14FiEQkQAKs5RT7PuubZpmOp3N61lT113X5pQu7O5+y0MP7b7jnbK1TYMhSEC0f/UQKwfX9vCWN7V/9RArB9f2/qsfvPWdzYf/4+0//Gj+a89ef+E383/xqR/Bcx/54e/+vR/4w//wf1z/Gz/6jX8zf/Iv/G2GhXc8VH/yn+3+2GexYEA2i/rczeeeePyJz1+/fnJyv+taF7zzwTsJIZRl6YvCh+C8YxHNuWmaDPNFAFHX90UIW5tbO7sXL1+5vL1zcWMyGYwGRfBMbDAABCLGAhPehJphhWkJK2aGVxERvpIBGV/CWFIYlggwWFZt6vb87Oz0/unRvXt37tw9OLh1dnoa+6imIuKc8yEAiDF+6NPfh5Undx9+7q//+rvf+c6NrU1SSzAjZJhCdf/RM6z81F/8P+Yn91lTjrFrupwzEVxZDdc3xhubGxe2h5ONMBzBhZhyjLlfiDnHDKgwexdCVYSqdMNKysAh+BCKsOS8E3ELICaAGVBSLJhBoWYGkBERwCAigJkWsECALRBgBJjmHHOKMfddWzfT2Ww+O59Op+28yX1vOTGRZymc884LU2DvvQiLMEw1a059arumrevpdFbXs6apu66NMW5fvPjehx7ef8c7ZeuCDEf7jxxh5eDaHt7yxvavHmLl4Nre3/ih+x/896sP/KXyn36sfv4w//L/U//ez+ycnOfv+Fv3XviNXQDv/qu3P/+ru0z4KgwLljR36dkbNx9/7PGnrz99cv8kxuiDL8tqOKiqwaAoSl8EZgYTgD7FtmmSWVEEEMWcvXOj8Xhza2t/b/fCzs7W5ubaeG0wGhQh0AJAQowlIrwJM8MKEeGNmRkAIsKCYSljifFliiVGSrlru9PTs9uHdw4PDxaO7907PT3t+56YhZlFHAsxZdUUY84ZwK88//ew8v/+eN68sDVYK2B26QN3sXLjZ9PdmzeOnrt5+8Xn5yf3WVOOsWu6nDMRXFkN1zfGG5sbF7aHk40wHMGFmHKMuV+IOccMqDB7F0JVhKp0w0rKwCH4EIqw5LwTcQsgJoAZUFIsmEGhZgaQERHAICKAmRawQIAtEGAEmOYcc4ox911bN9PZbD47n06n7bzJfW85MZFnKZzzzgtTYO+9CIswTDVrTn1qu6at6+l0Vtezpqm7ro0xbl+8+N6HHt5/xztl64IMRyAB0f7VQ6wcXNvDW97U/tVDrBxc23v+MP/APzo1YDzgn/i7k5/+zflvf6r9pgf9Jz/dPfnhneDpe77vk+7CA7/0ww/E5/+/s1/7Xy/8g38Ow4IlzV169sbNxx97/OnrT5/cP4kx+uDLshoOqmowKIrSF4GZwQSgT7FtmmRWFAFEMWfv3Gg83tza2t/bvbCzs7W5uTZeG4wGRQi0AJAQY4kIb8LMsEJEeGNmBoCIsGBYylhifJliiZFS7tru9PTs9uGdw8ODheN7905PT/u+J2ZhZhHHQkxZNcX4yzf/Dlae3H34+gd/8eu//t2bF7ZILcMUlkkV0P1Hz7Dyob/0j89P7qHvY9d1ba+W2cloPNna2V3bujCYbPhqABeSouv6tk+x72NMmjMBXiSEohwMwqDkQSFFYO8luDKU3gcfxDsv4liECCtkZoDBVJMpADYmYmIQE4GJCEwA2AyAEmAE05xj36c+9V3bNV1dz+p63szqtm1y7JEzgxyLZ/HOOyYvXkS8EyaYaVaLsY9N07TNfKGuu7bu+i7GtH3x4je//+Hdd3ydbF2QwXD/kSOsHFzbw1ve2P7VQ6wcXNt7/jD/wD86NWA84J/4u5Of/s35b3+q/aYH/Sc/3T354Z3g6a//LydE+Cc/uImvZIAhp5zb+MwXnnnsE594+nPX75+e9X3vQxgOq8n6ZDQeD6pB8F6xYAmWU+r6PpuSCICsWViKslxbn2xvb29tbS5MJpPheDwsK1/6IEJEAIjxCibC66kZVkwNryImpiW8nplhhYhggAGKJcaXKbJpTqmp2+n59Ojo6NbLt24dHBzePpydT1NOTFwUhXjnWECAIWnOaSHnnH7l+b+HlU/9aL9zYXuwUcH00gfuYuXGz+fjZ2/eff7Z288/d35yD30fu65re7XMTkbjydbO7trWhcFkw1cDuJAUXde3fYp9H2PSnAnwIiEU5WAQBiUPCikCey/BlaH0Pvgg3nkRxyJEWCEzAwymmkwBsDERE4OYCExEYALAZgCUACOY5hz7PvWp79qu6ep6VtfzZla3bZNjj5wZ5Fg8i3feMXnxIuKdMMFMs1qMfWyapm3mC3XdtXXXdzGm7YsXv/n9D+++4+tk64IMhiAB0f7VQ6wcXNvDW97U/tVDrBxc28MfJ77w1Nmv/s8USqR+8r0/5C59Awww5JRzG5/5wjOPfeITT3/u+v3Ts77vfQjDYTVZn4zG40E1CN4rFizBckpd32dTEgGQNQtLUZZr65Pt7e2trc2FyWQyHI+HZeVLH0SICAAxXsFEeD01w4qp4VXExLSE1zMzrBARDDBAscT4MkU2zSk1dTs9nx4dHd16+datg4PD24ez82nKiYmLohDvHAsIMCTNOaWPfOH7sfLk7sOf/q6f+4Zv+sadC9uUTGEZObEpke0/eoaVD33Hh87vHaW2yf0SMRVlOdnY3L10ZbxxwVeVsnQZTdvXbdu3fR9TjEljIiCIL6pyNB4Xo4EfllQEOHLeexecd96Lc17ECQsYSwbYgqqZKmAKAi+QEBMTaIlBBBiWCDDALOW+7VPXpj61Xdu2Xdc2bdP0bZ/61nImM0fsnffiCy9MQgABIlhIOecUY993TVc3Tdc1Xd/FGFNOF3d33/v+hy8++KCsb1A12H/kLlYOru3hLW9s/+ohVg6u7eHPwgBDiinW/TNfuPH4xz7xuevXT0/vd33PIqPRaHv7wmSyPhyPCu9VLWPBFKawnDVqSjlrziD23g2Hw7WN9Y3Jxtr6eDReGw4H49FobW2tLAvHBAIRmL4EIHyZvUJtCWaqBkCcOFkiIrzKzACoGRMBIBAMUPybDH3XT2ezk5P7946O7ty5e/vw8OjevbOz05xzWS0NysoHDxAWzLKpJo2pb9r2Zz/732Hl938sbV/YCesFLF/5wF2s3PywHj//3NFzz9164dnze0epbXK/RExFWU42NncvXRlvXPBVpSxdRtP2ddv2bd/HFGPSmAgI4ouqHI3HxWjghyUVAY6c994F55334pwXccICxpIBtqBqpgqYgsALJMTEBFpiEAGGJQIMMEu5b/vUtalPbde2bde1Tds0fdunvrWcycwRe+e9+MILkxBAgAgWUs45xdj3XdPVTdN1Tdd3McaU08Xd3fe+/+GLDz4o6xtUDUAMov2rh1g5uLaHt7yp/auHWDm4toc/BQMMKaZY98984cbjH/vE565fPz293/U9i4xGo+3tC5PJ+nA8KrxXtYwFU5jCctaoKeWsOYPYezccDtc21jcmG2vr49F4bTgcjEejtbW1siwcEwhEYPoSgPBl9gq1JZipGgBx4mSJiPAqMwOgZkwEgEAwQPFvMvRdP53NTk7u3zs6unPn7u3Dw6N7987OTnPOZbU0KCsfPEBYMMummvRHf/97sfLk7sOf/q6fe8973rN9YYeiKVSRIywL4dIHz7DyM//Bz50dHcV2rrHXnNn50Wg02dy8uHdpMFmHC9kw72LT9W3TtV1MKac+xb63rF6kKsrh+mQ4GvlxxVWAgEScOBbxTsR5J0IsWDFT2IJmNTPAFMTCYBZiYQIzgRgAE1YIMACacuz61PU59l3f913fdm3fdLFrY9elGKEmRMH74ENVBEdimgEwYSHlnFPsY+z7vmu7ru9iippSVt3Z3XvfQ+/fefuDNJqgKvcfuYuVg2t7eMsb2796iJWDa3v4szAspD7FeXvjC8889vGPX79+/fTktOlaMI+Go93d3Y2tjbXxWigCACMiJiIWL1m17bq+72NKOWfV7EIYjceTyWR9Y2M0GhVVNRoNNieTwaCSJWIiApEQETMRQFgytQW1bAZVM5iZGjE7v+C88yJMRFgxMzUAxiAQCATDkmLBDGpmqimm+XR2dHTv7t2jWwe37h3dOz07bZtGc/bOj8bjwWBQlgURpZwtq2LBDOi67vz8/EOf+T6s/P6Ppt3dXR57qD7wyB2s3PwFO3nxhXsvPPfys8+eHR3Fdq6x15zZ+dFoNNncvLh3aTBZhwvZMO9i0/Vt07VdTCmnPsW+t6xepCrK4fpkOBr5ccVVgIBEnDgW8U7EeSdCLFgxU9iCZjUzwBTEwmAWYmECM4EYABNWCDAAmnLs+tT1OfZd3/dd33Zt33Sxa2PXpRihJkTB++BDVQRHYpoBMGEh5ZxT7GPs+75ru67vYoqaUlbd2d1730Pv33n7gzSaoCpBDKL9q4dYObi2h7e8qf2rh1g5uLaHPwXDQupTnLc3vvDMYx//+PXr109PTpuuBfNoONrd3d3Y2lgbr4UiADAiYiJi8ZJV267r+z6mlHNWzS6E0Xg8mUzWNzZGo1FRVaPRYHMyGQwqWSImIhAJETETAYQlU1tQy2ZQNYOZqRGz8wvOOy/CRIQVM1MDYAwCgUAwLCkWzKBmpppimk9nR0f37t49unVw697RvdOz07ZpNGfv/Gg8HgwGZVkQUcrZsioWzID/7fHvxsqTuw8/+Z0/9a3f+q27u7sUTaHZUiTLxHTlg2dY+bn/6BfuH91NbWupB1CWxWQyGa9vrG1sFtVA2WVF06c25ZRyzpazxi7W8zr1EVnLshivTYbr42p9TapgDCMQkTCLCLOIEBETwQxZFWZZFWZqWGACMxGLMNMCM9MCACbCl+WcUtYYU0xd13d933Vt1zR907ZNE7tec2ZQVRaDohhUpRdvOQNwBDPNmlOKsY99H9u+jymmvKAw297be8/7H7r49rdhOEZR7D9yFysH1/YMS4Q/X4YlwpJhibBkWCIsGZYIS4YlwpJhibBkWCL8W2N4rf0PHGLl4Noe/owMqU9x3tz44o0nHnv86etPn5yc1G2jiuFgsLe3v7m1NVlfq6qKScSLc94H70NQ07rtuq5t2q6u5+fTqcJGo9H6xvr2zs7a2pr3oSjDaDCqqqIsgjgRJiIWZlpgBogBhZlpVjNVUzUzBRgQ50IIRRFCKLxzLLQAgwFqBgUzCATCkgEKU2Q1y9r2fT2fHR+fvPzyS4e3Dg8Obp2fn0OtKMLa2tpwNKqqgXMCQ4z9vK5jTEwEIhA1bXP/5OQjN74fK5/637vLly/TyEHzA4/cxcrNj9DZSy/eefH5g2dv3j+6m9rWUg+gLIvJZDJe31jb2CyqgbLLiqZPbcop5ZwtZ41drOd16iOylmUxXpsM18fV+ppUwRhGICJhFhFmESEiJoIZsirMsirM1LDABGYiFmGmBWamBQBMhC/LOaWsMaaYuq7v+r7r2q5p+qZtmyZ2vebMoKosBkUxqEov3nIG4AhmmjWnFGMf+z62fR9TTHlBYba9t/ee9z908e1vw3CMojBiEF26eoiVW9f2CH++DEuEJcMSYcmwRFgyLBGWDEuEJcMSYcmwRPi3xvBV7X/gECsH1/bwp2NIfYrz5sYXbzzx2ONPX3/65OSkbhtVDAeDvb39za2tyfpaVVVMIl6c8z54H4Ka1m3XdW3TdnU9P59OFTYajdY31rd3dtbW1rwPRRlGg1FVFWURxIkwEbEw0wIzQAwozEyzmqmaqpkpwIA4F0IoihBC4Z1joQUYDFAzKJhBIBCWpPweiAAAIABJREFUDFCYIqtZ1rbv6/ns+Pjk5ZdfOrx1eHBw6/z8HGpFEdbW1oajUVUNnBMYYuzndR1jYiIQgeiHf/eDWHly9+F/8e/+2Ld/+7dfvnyZoilMLUcyFcKlR0+x8uH/5JeO79yJ9RyavXeDshyNhsPx2nC8FqqBEkVFzBYzkmpOqkm7rp/P6tj3UPMhjMdrw/XxaGPNl0EFBoBARMLMRMxMRCA2U1VTzWZQU5hhhYmIF2SBmYiYCK9FRDAzzSlrjin2seu7tm27ebNU17HrNSUmKkIYhFCVReEcAQwwgbBgmlPfx7bv5vOmiwkLzExu59L+N3zb+7evXMZgiBD2H7mLlVvX9vAqwpsxs5wVgAgTEVbMTGGWDYAIExGWDDAsEZYMtgDFgjGBiAGYGQAiAggwLBGWDEuEJcMSAYQ/M8OXEL7EAJgpDCAmIgCG19r/wCFWDn5rD4QFM8tZAYgwEeFrZ0h97Or6mS8+8zuPP/75z33++P5J3czVMCyHF3a2tzY319fXR8NRURZFWVZV5X0g5yxb2/dt39RtO5tOT05PY4xFUYzGo82tC+O1UVVVhS9c8CGEQVWE4IWFRYSImUFYUigUZhmAAqYKA8AgceJDKIuiLErvPTsWJhgBUBiDmJl4gQCoQrOlmGOfUozz+ezk/v2ju3cODg7v3j48uncvdl0RitFotLG+vjYalYMhEbq2qet6OpvHlJwTAhts3jQnx8e/+vLfx8qnfqS/cvkyhgLTBz5wByvP/qJMDw+OXnj+pZs3ju/cifUcmr13g7IcjYbD8dpwvBaqgRJFRcwWM5JqTqpJu66fz+rY91DzIYzHa8P18WhjzZdBBQaAQETCzETMTEQgNlNVU81mUFOYYYWJiBdkgZmImAivRUQwM80pa44p9rHru7Ztu3mzVNex6zUlJipCGIRQlUXhHAEMMIGwYJpT38e27+bzposJC8xMbufS/jd82/u3r1zGYIgQjBhEl64eYuXWtT0AhDdjZjkrABEmIqyYmcIsGwARJiIsGWBYIiwZbAGKBWMCEQMwMwBEBBBgWCIsGZYIS4YlAgh/ZoYvIXyJATBTGEBMRAAMr2MwGIBLj9zGyq3f2s1ZAYgwEeFrZ0h97Or6mS8+8zuPP/75z33++P5J3czVMCyHF3a2tzY319fXR8NRURZFWVZV5X0g5yxb2/dt39RtO5tOT05PY4xFUYzGo82tC+O1UVVVhS9c8CGEQVWE4IWFRYSImUFYUigUZhmAAqYKA8AgceJDKIuiLErvPTsWJhgBUBiDmJl4gQCoQrOlmGOfUozz+ezk/v2ju3cODg7v3j48uncvdl0RitFotLG+vjYalYMhEbq2qet6OpvHlJwTAhvsR37/e7Hy5O7D/+Lf+/Fv//a/cOXyZYqmgJEpm5Lq/qMnWPmFv/Krdw9utdOZQNfGwzIEJhSDarKxUVZVBhk4gbMiZu371LV92/Zd0+WsIhJCMVhYG4/WR6EKIIBAAESYwLTAILzCVNVMVU2NAAOyZgOIyBE770jEMYMIrzADEYhBCjM1VbUY+6btm6aZz+qmrptZE/veUhaGFymYvZfCSSHOCQmRMHvvzCzGeD6bHx/fr7tevC9CVY2GFy9ffsf7vmVzbx9Vad5feuQuVm5d21MsMf4YOeWu6wAURSFOsGCWzVRzTIlB3nnnBDDAAMMSYclMNWddIAIxiTiAck4ARBwRAYYlwpJhibBkWCKA8Aoj/IkQXmGAmmGBwFggAGoLukAgFhEQFgx/ZP+RQ6zc+uguEYGQUo6xB+B9ECf4CoTXMLyGpdg38/mzN575nSeeePr600f3jrq2dyKhrMbD0WQy2djcXJ+sry2M14bjNedcSvkVXd93fT+r6/PZtOu6rCYsvizW1sZbWxeqqko5O+FBVYUFLwtOBIAaVLOpwgxEICaCEIEZgKkyMTspihBCUQTvnGdhAoEAImEJbom9AJQVXZfqumnqpq3r09PT27cPjo+PZ2ens+m0qWsYhoNB4YN4HpbVeDwBMD0/PZ/N5vNaYVVVgajr+tl8fnZ29lvHP4iVP/hx293bRcWAPXD1NlZe+OWiuXd09OLzNz//9N2DW+10JtC18bAMgQnFoJpsbJRVlUEGTuCsiFn7PnVt37Z913Q5q4iEUAwW1saj9VGoAgggEAARJjAtMAivMFU1U1VTI8CArNkAInLEzjsSccwgwivMQARikMJMTVUtxr5p+6Zp5rO6qetm1sS+t5SF4UUKZu+lcFKIc0JCJMzeOzOLMZ7P5sfH9+uuF++LUFWj4cXLl9/xvm/Z3NtHVZr3SgyiK1cPsfLStT3GHyOn3HUdgKIoxAkWzLKZao4pMcg775wABhhgWCIsmanmrAtEICYRB1DOCYCIIyLAsERYMiwRlgxLBBBeYYQ/EcIrDFAzLBAYCwRAbUEXCMQiAsKC4Y+YmloGcOWDR1h58Td2YuwBeB/ECb4C4TUMr2Ep9s18/uyNZ37niSeevv700b2jru2dSCir8XA0mUw2NjfXJ+trC+O14XjNOZdSfkXX913fz+r6fDbtui6rCYsvi7W18dbWhaqqUs5OeFBVYcHLghMBoAbVbKowAxGIiSBEYAZgqkzMTooihFAUwTvnWZhAIIBIWIJbYi8AZUXXpbpumrpp6/r09PT27YPj4+PZ2elsOm3qGobhYFD4IJ6HZTUeTwBMz0/PZ7P5vFZYVVUg6rr+H3/mb2Llyd2Hn/zOD73/oW/b3dulaEoAw8gUWfcfvYeVX3r0o7dfenF2el8sT8bj0ruUex+Kza2NcjAAM0ggPoNStr5LddO1Tdd1MWfzS6GsyuFoOF4fhUEBAghMjFcxExGDsGQwU1VTUyyYpZQNCjAzeefFiRMBEWAwmGGJAQIYBqhpjKnv+vm8mc3mTV3XsybF3rIKTJg8UyAqvBsU3jtxRAQIIaXctN1sPj8+PY8xS1FW1XA8mWxfeeDB975vc28PRWHOXXrkLlZeurZnWCKA8VWoGQAm6vp+Optq1rIonDgwMbMIq1lOmQneeREmAmAwMxhAAAjIpprV1IiMiEBCgEEBiDgiAgxLhCXDEmHJsEQA4RVGAOFrZjAARGSAmgGGBSIGAVBb0JQzA0wiIDVjgIgBGOzSo3ewcuuju0QEQkq56zoARVGIE3wFwmsYXmWApRS7trn5zI3ffeyJ65/77N27R03TMrN3vghhfW1zd29v++LO5sbmcDj0EjLQNm1MyQyqOebc97Ht2q6POavCAAzGo4u7u4Oy6vqeCVVRlUVwQbxzIgIgq6lm5GwGEEjEsRARCwNQVQZYxDnvvfPOixNmJiJmJiYWV3rvgxfvFWi73DbddF7X83kzn90/Obl7+/bp2f2unseu06yFd4PhMDhnpt65YTXMOZ+e3p9NZ03Xgnk4HIJoXjfz+fxsOv2/pz+ElX/1k7Rz8aIVRMCVq4dYefGfDeLp/XsvvXjj6eu3X3pxdnpfLE/G49K7lHsfis2tjXIwADNIID6DUra+S3XTtU3XdTFn80uhrMrhaDheH4VBAQIITIxXMRMRg7BkMFNVU1MsmKWUDQowM3nnxYkTARFgMJhhiQECGAaoaYyp7/r5vJnN5k1d17Mmxd6yCkyYPFMgKrwbFN47cUQECCGl3LTdbD4/Pj2PMUtRVtVwPJlsX3ngwfe+b3NvD0VhzimxET1w9RArL17bI4DxVagZACbq+n46m2rWsiicODAxswirWU6ZCd55ESYCYDAzGEAACMimmtXUiIyIQEKAQQGIOCICDEuEJcMSYcmwRADhFUYA4WtmMABEZICaAYYFIgYBUFvQlDMDTCIgNWOAiAEYDGZqCqIrHzzCyou/sdN1HYCiKMQJvgLhNQyvMsBSil3b3Hzmxu8+9sT1z3327t2jpmmZ2TtfhLC+trm7t7d9cWdzY3M4HHoJGWibNqZkBtUcc+772HZt18ecVWEABuPRxd3dQVl1fc+EqqjKIrgg3jkRAZDVVDNyNgMIJOJYiIiFAagqAyzinPfeeefFCTMTETMTE4srvffBi/cKtF1um246r+v5vJnP7p+c3L19+/TsflfPY9dp1sK7wXAYnDNT79ywGuacT0/vz6azpmvBPBwOQTSvm5+/8bex8uTuw5/5rl/45m97387FixRNCWADQZF1/9EjrPyf3/OxwxdfOL13N/X9qAzeSU59KMPmxkY1GJB3znvxBdhlQ4xaN13T9E3baTIW550vyjAcDyebk6oqwQAUgJrBoAYmMBOxMDERzDTnrGpqgOWsamYAhESciBMnQiwAzNTUzFRhYLATEBTIKXdd3zTtfGHWNPMm9j1yhplnCkyeqQp+WBWld0zIMc6n0/lsPqvrruujAuzEh6IajsaT7Qfe9nXve9/m/j6KwsRdeuQOVl68todXEcB4HTXTnAGwSNs2x8fHbds5ERZxwkVRDkbDwgXAAJgZEUSYADNdMBhAwgRA8SWmBhgAArEwEeFrQgBhwQggfG3MLKsCEGEiMgBmGQZAiAACkFVNMwwEgpqlRCAJHiDN6cp/doyVg9/awwIhq2rOAFiEmfEGyPB6BpiZ5pyevXnj8Y99/Kmnnjq8fXt2PksxAuxFdnZ23vXur798+crWxgVmPj07nU5n87rNMYFZiCEMkMIAAjNABhsMh1vbF4sidF3DRIOyKorgnTjnhQlgQ86qlgxQMBOTMBOEGEQEgAlEDCbHDBIiMC0wMTkn4nzhl8T7mPRsVs/nTdM2bdPU8/l8Oj07uz+bnjezmaboRMoQhoOq8EFEGNCsTdtOp+dNXXcxsshgNARRPW+mdT2bzT5W/zBW/vAf88Xdi9mBgCtXD7Hy0q+MdHp+fOvWzc8/ffjiC6f37qa+H5XBO8mpD2XY3NioBgPyznkvvgC7bIhR66Zrmr5pO03G4rzzRRmG4+Fkc1JVJRiAAlAzGNTABGYiFiYmgpnmnFVNDbCcVc0MgJCIE3HiRIgFgJmampkqDAx2AoICOeWu65umnS/MmmbexL5HzjDzTIHJM1XBD6ui9I4JOcb5dDqfzWd13XV9VICd+FBUw9F4sv3A277ufe/b3N9HUZg4JTKiB64eYuXFa3sACGC8jpppzgBYpG2b4+Pjtu2cCIs44aIoB6Nh4QJgAMyMCCJMgJkuGAwgYQKg+BJTAwwAgViYiPA1IYCwYAQQvjZmllUBiDARGQCzDAMgRAAByKqmGQYCQc1SIpAED5DmBIBFIHzpkdtYeemjFzVnACzCzHgDZHg9A8xMc07P3rzx+Mc+/tRTTx3evj07n6UYAfYiOzs773r311++fGVr4wIzn56dTqezed3mmMAsxBAGSGEAgRkggw2Gw63ti0URuq5hokFZFUXwTpzzwgSwIWdVSwYomIlJmAlCDCICwAQiBpNjBgkRmBaYmJwTcb7wS+J9THo2q+fzpmmbtmnq+Xw+nZ6d3Z9Nz5vZTFN0ImUIw0FV+CAiDGjWpm2n0/OmrrsYWWQwGoKonjcfef7vYOXJ3Yc/+90f+eZved/F3YsUVQEIQGbQvP/oEVau/TefuvX8s0cHt9r5rHTCDGj0wa+trZWDyoXgQuGLksSDOGV0fer71LURIBZfhODLMBgO19YGRekBKNTMVDXnrGZMJMIiTkSI2EzzgppaRtYMg2FBiNiJsDgnxAwiW8gLmi2D2BdCIkaIOcc+d207m9dN3bTztu8jcmZTRySMQFQ4qYLzQmxW1/P7x8fnp2fT2Syp+WLgi1JCUQ3XJpsXLl55+9vf+02T3T2EYCKXHrmDlZeu7eE1GK+jSzmlHFM6OTl5/oXnT09OzTKROCej0Wh7e3t9Y31tvFYWhZkREQthQdVggGGJCAQG01JW05wAsDhhxp8AYcEIIHxtzCyrAiAQM0AEQM1AYNCCAWpmWQGDAVk1RgJxUTCRpnTlrx5j5dZHd7GiMLyCiInwBsjwegaYmarmF5579onHHnvqqadevnXr9Ph+3/dQE+d2Lmy/653v2t27NJlMctbj4+PZdNb1SZOCwCLivHNCIuycd4GFs1lZDjYvbIci9H1DRFVZVSF4JyLMJGCYalYzU5gRCxOYhIQACDOREOOPmMIsAyAiFvHOe+/EeSeOnXQxn09n83nddn3TNvVs1s7nTVvXs+n8/FxTLLwviqIqijIEHzzMUoxN087ns7btNCs7HgyHCszmC/W8aT7e/EOs/Ouflos7O0mw8MAHDrFy69cmWs9Pb99+/otfuPX8s0cHt9r5rHTCDGj0wa+trZWDyoXgQuGLksSDOGV0fer71LURIBZfhODLMBgO19YGRekBKNTMVDXnrGZMJMIiTkSI2EzzgppaRtYMg2FBiNiJsDgnxAwiW8gLmi2D2BdCIkaIOcc+d207m9dN3bTztu8jcmZTRySMQFQ4qYLzQmxW1/P7x8fnp2fT2Syp+WLgi1JCUQ3XJpsXLl55+9vf+02T3T2EYCJKdOUDt/GWP5WXPnoRC0RMhDdAhtczwMxUNb/w3LNPPPbYU0899fKtW6fH9/u+h5o4t3Nh+13vfNfu3qXJZJKzHh8fz6azrk+aFAQWEeedExJh57wLLJzNynKweWE7FKHvGyKqyqoKwTsRYSYBw1SzmpnCjFiYwCQkBECYiYQYf8QUZhkAEbGId957J847ceyki/l8OpvP67brm7apZ7N2Pm/aup5N5+fnmmLhfVEUVVGUIfjgYZZibJp2Pp+1badZ2fFgOFRgNp//8kv/ACtP7j78+f/0F9/zze+9uLNDXc4EMJGYQXX/0btY+cT3/+uDZ5956bnnZqf3yTJDmcw5CWVVFMEVhS/KUJQuBBJv4JyhRgpy4quiKouqGFZFEcSTOIDMTLPmlHKMySwTiRPxITgnRARYyppz1pxNDa8iBrMIizhhESaoQXNOKcaUiMmXpfNiRNksx9x1fdO0TdN1TZv6qCkzVAABHMBQzlFz1BRn0+nx0dH56Vld10ryk/2H8ZY/Vy/+820QACImJlLCAoPw1ZDh9QywnLOm/sUXXvi9f/mpz3z2sy889/zx8XHfRQBlUU7Gk+2trdF4zfsAIPa9Ghx7BWnORiAWJ8JOxPuiKFmcwoqy2tjcLIoi50REVVH64LyIMBMTAFPLms2wIEQsTCxMoAVm7xwxmwLQrKYpJ81mSsSOxQfnnBcREBQUY66brumaruvr+Xw2nTZ1nfu+qWdnJyex74sQnJPCife+CN6JMFFKqW6a2EdVFeeqqkpm0+lsPm+avv1Y8w+x8ukPue3t7Z6NgLc9chsrB7++YV07Pbr78s1nDp595qXnnpud3ifLDGUy5ySUVVEEVxS+KENRuhBIvIFzhhopyImviqosqmJYFUUQT+IAMjPNmlPKMSazTCROxIfgnBARYClrzllzNjW8ihjMIizihEWYoAbNOaUYUyImX5bOixFlsxxz1/VN0zZN1zVt6qOmzFABBHAAQzlHzVFTnE2nx0dH56dndV0ryWA0KQajoqwmG5t7V96297YHL777XcMLF+A8mI3o0gdu4y1/Ki999CITKWGBQfhqyPB6BljOWVP/4gsv/N6//NRnPvvZF557/vj4uO8igLIoJ+PJ9tbWaLzmfQAQ+14Njr2CNGcjEIsTYSfifVGULE5hRVltbG4WRZFzIqKqKH1wXkSYiQmAqWXNZlgQIhYmFibQArN3jphNAWhW05STZjMlYsfig3POiwgICoox103XdE3X9fV8PptOm7rOfd/Us7OTk9j3RQjOSeHEe18E70SYKKVUN03so6qKc1VVJbPpdPZPD/4HrDy5+/Az//mvfuN7vn57e5uamARgITFAdf/Ru1j55N9/+uWbzzx344un9+5qSmxZhMqyHI5G1aB0IfhQuKJgCRAxElMCmFi8D1U5KMqyLCoXxJDBSmxqGlOKMabYZzUCOe+LIgQfiAmA5oWUUjbFK4gBIiYWFueFWITJzFLKfez72IOpqioXPAgK5IQYY9v0MfZ926c+QTM0k5mokilijH0T+6arm/l0dnZ6MpvOmqYxyE/pP8Fb/ly9+OtbRgARizAzYAowCF8NGV7PAMs5a+xffvnFJ//gyc999jNfvHHj6O5R30UiDIpBsRAK7z2LhFAMqmFZlt6XALq+y6ogFmYW8UWoqiE7D1hRlpP1dV+UZllEihAK75iZCAtmUM0wqCoviAgRC4MWWJi89ywCwFRTzjktZFMlZufE+SAsxFDVlHIXU922Tdt1TTOr57Pzaew6qHZtc356qjGG4L0TIfZOihAcMwE5p6ZtY0pqcCK+KHLOZ9Np07Yp5493P4KVT/+M375woYUK8LZH72Dl4P/aQozz+/fuPHvz5ZvPPHfji6f37mpKbFmEyrIcjkbVoHQh+FC4omAJEDESUwKYWLwPVTkoyrIsKhfEkMFKbGoaU4oxpvj/swf3MZaf133Yv+ec5/n97r1z587Mzszu7AyXS4oURYqkJYq0ZFpKLAW2m9i1KLIN4Lh/FGiBtmiDBKnTFGiAOnbTBCiS2E3tJrEdtIljWbZkM6KjJI7TNmlsRCJXfN1dLsnlvu/svOy83bn3/l6e55zTmaHXpmhSiYL+mc+nVXMChRjLsihiQUwATA/knNUN7yAGiJhYWEIUYhEmd89Z29S2qQVTt9sNRQTBAM1IKdVVm1Lb1m1uM0xhSu5iRm5IKbVVaqtmUo33R3u726P9UVVVDun0+rHbK7rd4yeWP/yRh07ed//MqVOduTmwgNmJVj6/hn/v38n1r55gZsANYBDeDzm+lQOuqpbaGzeunXn+zLmzr7351lubG5ttk4jQK3vlgaKMMbJIUZS97lSn04mxA6BpGzUDsTCzSCyLbneKQwS87HRmZmdj2XFXESmLooyBmYlwwB1mCoeZ8QERIWJh0AEWphgjiwBws6yq+YC6GTGHICEWwkIMM8tZm5QndV3VTVNVo8l4NNxPTQOzpq6Gu7uWUlHEGESIY5CyKAIzAaq5quuUszmCSCxLVd3b3/+N7Z/EkTNLT1z+U7/2wEc/sriwQJOUBCRCAkBt+ekNHPnGX7x08+JbF994fWt9XVMr0FiE2dm55ZXl2dkZioFDEAlOnJ2yuirMAVCQGGNZxiIWkQROBjgJ1FWztqltm9ZMmSXGUHY6RSxYBIDmpJpzVjcDmBggYmIQRCQEERZmcbecc5PapmlB6PbKECOYHDCDZk8padac1JKaKlSh2XOGppxabaqmGo/296vxpKmq1NRNyinb39j/2/j3/n917Svz2Z2ZJARhdnw75PhWDri7memtm6svf/OFV15+5dy58+ura21KROiUPQG1OTFxjMXs3LFTK3cNZmaZQ855UlU5JXcHsQSJZWeq34+xABDLsj+YKTsFEyRIGYoQhQjugJuZq6q5ARDmEAIxBxHQARYmCVFYWMjdTTWr5qzuFkRYJMZIRKaecltV7aSpRuMDk8lkPBlPxuOx5hyZodrUFRmKMsQQInMhEmM0t6aum/pQSlndmDgURau6vz9q2paE/qX9TRx57eeL+YX5ilRA9zy9jiOrv7EA0/HO9vaVyzcvvnXxjde31tc1tQKNRZidnVteWZ6dnaEYOASR4MTZKaurwhwABYkxlmUsYhFJ4GSAk0BdNWub2rZpzZRZYgxlp1PEgkUAaE6qOWd1M4CJASImBkFEQhBhYRZ3yzk3qW2aFoRurwwxgskBM2j2lJJmzUktqalCFZo9Z2jKqdWmaqrxaH+/Gk+aqkpN3aScsmUHJBad7sqp0x97/JOn7r+/PH489KfBAmYnWvn8Gu5Y/c2T+MMc7q6m165c/Ve/8ztf/9df/8bzz6/eXBUhBrNQt+wOZgf33HPPYx/7+EOPPPzAAx9ZOnmi1+8HCe4ZAEsQZnfLOddNC6DbKUMI7jhAhCOEQ45DhEOOQ4RDjkMEEL49x/tyd5gbwACYABDgZmoGgoiYueY02h9t3d6ajEaWtYjF9GC6Pz3dHwyKsgSTw1eeWsMd1796Qpgd3w45vpUD7m5meuvm6svffOGVl185d+78+upamxIROmVPQG1OTBxjMTt37NTKXYOZWeaQc55UVU7J3UEsQWLZmer3YywAxLLsD2bKTsEECVKGIkQhgjvgZuaqam4AhDmEQMxBBHSAhUlCFBYWcndTzao5q7sFERaJMRKRqafcVlU7aarR+MBkMhlPxpPxeKw5R2aoNnVFhqIMMYTIXIjEGM2tqeumPpRSVjcmDkXRqu7vj347/XUcObP0xNU/9ZUHHnpgfmGeJikJSIQEgNvyUxs48sJPXL918eKbF85tra/ltg1k3U4xPTNYPHF8MJiWGDnGECNIFGwGVTiIWILEKEUQoUDMMGQQIICZmjZtTm2dVYUlxtjplEVRsAgAU82HVM0YIGYQMTEziCUECSLE5OY5a5PapmlBVnY6EgUEOLnDDJZd3Uwdaq6qOVvbatukts5NneqqHo/G+8O2rmFuqkm1TfpXNn8GR376iZ974lPfe+rDD8ixOelNQQKYl59ax5HV3zyJD+JQNc3pwoULX/va1373d3737LlzO1vb3U4niDjAwhLC8YXFBx544KGHHnz40Ufvu//+U3ff1Z+edjcAREJMTFDVpmkBlGUhIkQEEOA4RDjkOEQ45DhEOOQ4RPg3crwvd4e5wQEwiJhgUFdtc9KUcq6ran9/f2dre/Xmzb29PagVRWdububE0tLdp08/+l8a7rj26wvmDoKEIMyOb4cc38oBBxzua6s3X/7miy+//NK5186u3rzVtgnmRVEAaNtELFPd3vz8/MpdpwbTA1Vv23YyqXJOxCwhxKLo9qYGs7Nl2XFCiLHbnYpFIQwJ0ikKYTK4m+WsampZzR0EESliwSJBhJgACDOLBBEiAeCuWdVUAbBIDAcigJxT0zSTqhpNqgOj0Xi4vz8Zj5uqMtUYhAG4B6IYQxlCFGYRIUptO9zbG41TYu/eAAAgAElEQVQnTV01KbkqmClIVp1MKjXETvH/+v+GI2d/oTw2f6wiFdA9T6/jyOqzC3Cvtne2r1+9dfHimxfOba2v5bYNZN1OMT0zWDxxfDCYlhg5xhAjSBRsBlU4iFiCxChFEKFAzDBkECCAmZo2bU5tnVWFJcbY6ZRFUbAIAFPNh1TNGCBmEDExM4glBAkixOTmOWuT2qZpQVZ2OhIFBDi5wwyWXd1MHWquqjlb22rbpLbOTZ3qqh6PxvvDtq5hbqpJtU06aVqFxLI8dfreJz71vac+/IAcm5PeFCSA2YlWPr+GO1Z/8yT+MIeqaU4XLlz42te+9ru/87tnz53b2drudjpBxAEWlhCOLyw+8MADDz304MOPPnrf/fefuvuu/vS0uwEgEmJigqo2TQugLAsRISKAAMchwiHHIcIhxyHCIcchwr+R4325O8wNDoBBxASDumqbk6aUc11V+/v7O1vbqzdv7u3tQa0oOnNzMyeWlu4+fXrm2DEJQkzLT63hjutfPSHMjm+HHN/KAQcc7murN1/+5osvv/zSudfOrt681bYJ5kVRAGjbRCxT3d78/PzKXacG0wNVb9t2MqlyTsQsIcSi6PamBrOzZdlxQoix252KRSEMCdIpCmEyuJvlrGpqWc0dBBEpYsEiQYSYAAgziwQRIgHgrlnVVAGwSAwHIoCcU9M0k6oaTaoDo9F4uL8/GY+bqjLVGIQBuAeiGEMZQhRmESFKbTvc2xuNJ01dNSm5KpgpSFadTKrfiX8HR84sPbH6n/7DD91/37H5YzRJSQBmFnJSX356A0e++ZOrq29fvPTG+a2NjZzbwOh1Cg6iqhKk7HbKXrfT68WiE0LBoRCJEooQCglFkBCIQQDUoGCD4ICZpwNtY2bMEkLsdIoYCxYB4KaaNWVVUwaICcRMxMzEFEIUFmaYWc7aptym2t1jUbAIyMwZBncYDhA7m5mrats0dZWqqq0m9XjcTkbVZNJUI8vKRExspm3Wn1z96zjy09/9t5745KdW7v9wnJ2VqSmEAiLLT63jyOpzJ/HBcs6paV579dVf+/KXv/H1r9+8udrUzfR0P0hw95RTTrmIxez8sbtPnXrwoY88+rGPPfHEd6/ctcLCAJkZAAnCBFXDEWJiYiLCvxPH+yO8i+Pd9EBKMOcYhIVA2TTV9XA43Njc3Fhfu3Xr1urqrbXV1eHevmUty2J2bu7+D9//5JNPfv9PHMcdN589oXAAIkxEjm+HHN/K4Q444Gurqy+98MJrr7x67vyFW6trTVNrzgQilijS7U4NpmcGg35/qk8sVVXXddM0NcBlt9vrdbv9qdnZufmFxd5U32DMLCEEZrDEILEIwqymmjXllFNOOcEdRDHEoigkSJTAwqADzAQm4SAA3M3VsykTJIQgMUYBkFKq6mYyHjdtm7KOJ5Od7Z3JeNQ0takGQETKGGOQwCTChQQhypon++Ot7a394d6kqpq2VVUQOIgZqqaVINMzM/9X+hkcufB/9gYzMxVUgHue3sCR1WcX4F7t7u5ev7769sVLb5zf2tjIuQ2MXqfgIKoqQcpup+x1O71eLDohFBwKkSihCKGQUAQJgRgEQA0KNggOmHk60DZmxiwhxE6niLFgEQBuqllTVjVlgJhAzETMTEwhRGFhhpnlrG3KbardPRYFi4DMnGFwh+EAsbOZuaq2TVNXqaraalKPx+1kVE0mTTWyrEzExGbaZp3UScFclCun7nnik59auf/DcXZWpqYQCoiAaPnza7hj9bmTeD8559Q0r7366q99+cvf+PrXb95cbepmerofJLh7yimnXMRidv7Y3adOPfjQRx792MeeeOK7V+5aYWGAzAyABGGCquEIMTExEeHfieP9Ed7F8W56ICWYcwzCQqBsmup6OBxubG5urK/dunVrdfXW2urqcG/fspZlMTs3d/+H73/yySdP33tv2euGGJafWsMdN59bIiLHt0OOb+VwBxzwtdXVl1544bVXXj13/sKt1bWmqTVnAhFLFOl2pwbTM4NBvz/VJ5aqquu6aZoa4LLb7fW63f7U7Ozc/MJib6pvMGaWEAIzWGKQWARhVlPNmnLKKaec4A6iGGJRFBIkSmBh0AFmApNwEADu5urZlAkSQpAYowBIKVV1MxmPm7ZNWceTyc72zmQ8apraVAMgImWMMUhgEuFCghBlzZP98db21v5wb1JVTduqKggcxAxV035z8Pdx5MzSEzv/1T89dc/dg5kZqlJmgBkCkPny0xs48uJPra5dvnzlrQtbtzc9t0ToliGlvLW3Y6bd/oGpXn+67PSKshOLMsYyFp1YdGIoghREOKJOCeQkBAIcKWtOrbuDKIgURRFDlCBE5O6aNam6KgBiAMSHiFiCCDEDcLecVXNOOQEuIYAZMDW4MxygAywkcHjW1FT1ZFyNx/V4NBntV/vDphprat1ccEjd3PFTt34aR37u07/4yHd9fOne+7oL86E/jRAhsvzUOo6sPncSHyy1qRqPX3zxxS/+yhfPvPDN4XDPVLvdXhRRd0upTdndWOTY3Nzd99zzyHc98ulPf+YjD35kfn6+6HY1K+DMIkIH3N3c3UBMTCAifOcc74/wLo530wNNC4BjEGJzr+t6uLOzunrr0qXLV69cvnHjxtra2vbW9mR/nDUXRRgMZh5+5OE/8UM/9GM/9wjuuPncEhwGxwEiJsIHI8d7ucMMsLVbt1558eVzZ187f/7C2upqNZm0bXKHkJSdzlSvNxgMyrLLhJy1rtqmbVNuJYR+f9AfTA8Gg/mF+eNLK1NTfTAccDNiJoKIlEVBjJxTSrlt26ZtNSczZ5EYQxmLWMQQCyEiZgBEAJEwE5G7m8NNQRQlhigSAgDNuW7auqqats1qk8lka3trMhrlpnXN5laG0Ot2yxAAD8JljORITTPc39vY2Njd2RtX47ZpW80OhCAOpKxlpzu/uPiPx/8Ljrz1S9NT/X4NY+Cep9dxZPXZBbhXe7t716+vXb585a0LW7c3PbdE6JYhpby1t2Om3f6BqV5/uuz0irITizLGMhadWHRiKIIURDiiTgnkJAQCHClrTq27gyiIFEURQ5QgROTumjWpuioAYgDEh4hYgggxA3C3nFVzTjkBLiGAGTA1uDMcoAMsJHB41tRU9WRcjcf1eDQZ7Vf7w6Yaa2rdXHBI3dyRnMFBis7JlVOPfNfHl+69r7swH/rTCBEiIFr+/BruWH3uJN5PalM1Hr/44os/9Xdv7J776eFwz1S73V4UUXdLqU3Z3Vjk2Nzc3ffc88h3PfLpT3/mIw9+ZH5+vuh2NSvgzCJCB9zd3N1ATEwgInznHO+P8C6Od9MDTQuAYxBic6/rerizs7p669Kly1evXL5x48ba2tr21vZkf5w1F0UYDGYefuThP/FDP/TwI49Mz82UZWfl6XXccf2rJ0DERPhg5Hgvd5gBtnbr1isvvnzu7Gvnz19YW12tJpO2Te4QkrLTmer1BoNBWXaZkLPWVdu0bcqthNDvD/qD6cFgML8wf3xpZWqqD4YDbkbMRBCRsiiIkXNKKbdt27St5mTmLBJjKGMRixhiIUTEDIAIIBJmInJ3c7gpiKLEEEVCAKA5101bV1XTtlltMplsbW9NRqPctK7Z3MoQet1uGQLgQbiMkRypaYb7exsbG7s7e+Nq3DZtq9mBEMSBlPXlY1/CkTNLT9R/7l+eWF6a6vepUSWA4ALAbOXpTRx58adW169euX750u72bcsJrkyo62pzezur9gf96al+b3q67PViLCQWIRYxdsqyW4QihkDEOEAKNmKQMOOQuplmVQMgwhKCSIwiLATA3FXV1NwcR0goiBCzsOCImh4wU1cDQ0RAMHc1uMJBYGYiIYHDVdu6mYz269H+eLQ/Gu6N9rZz0wjBTVPTppzMnGPx17b/dxz5u9//D07fc//i6XunTy53ZmcgAvDy0+s4svrcSQA/+5XRn/6P+/hD6rre39375pkzv/IrX3z5pVcmdWVZCSACwIFJJLpZk9oixrnZubvvu/djjz322Cce+9hjHztxYsnc3RxwgETogLubw80BiNABfIcc74/wLo53c7UDgDOLHUhpe2v76pXLb7751rmzZy9fury2vjYcDlObczrQEkun13300UeeeeaZP/Orn8Udq8+ddLi5uxkAEmEifAByvJcDpq55Y33jjfMXzr9+7vzZ8zevXx/u77cpkUNYQoidoii7XSZJOZk6HGaWVIuimJ6ZGRyZX1xcXjk1GEwDbDDVrA5hEmYpAuCpTU3bNE2b2ia1CQAHibEoyyLGIobAwgCYiJgZRMwA3Mzg7iBCEAkhxBiJKKtqzk3btk3btmk0Gu3sbI/GI0vZclbNMchgqlfGCLgId2MJ96ath7u7a7fWtnd2JpNJU7fJEuAcChDM0Z+eXlpe/srmT+LI21+c7Xa7LTsBp59aw5HVZxfgXu3t7l2/vn71yvXLl3a3b1tOcGVCXVeb29tZtT/oT0/1e9PTZa8XYyGxCLGIsVOW3SIUMQQixgFSsBGDhBmH1M00qxoAEZYQRGIUYSEA5q6qpubmOEJCQYSYhQVH1PSAmboaGCICgrmrwRUOAjMTCQkcrtrWzWS0X4/2x6P90XBvtLedm0YIbpqaNuVk5hyL3tR02evHTm9+8cTpe+5fPH3v9MnlzuwMRACG0PLn13DH6nMn8X7qut7f3fvmmTP/xS98dO6tH5nUlWUlgAgAByaR6GZNaosY52bn7r7v3o899thjn3jsY4997MSJJXN3c8ABEqED7m4ONwcgQgfwHXK8P8K7ON7N1Q4Azix2IKXtre2rVy6/+eZb586evXzp8tr62nA4TG3O6UBLLJ1e99FHH3nmmWcef+K7F04s9Pr9U89s4o5rzy4CIBEmwgcgx3s5YOqaN9Y33jh/4fzr586fPX/z+vXh/n6bEjmEJYTYKYqy22WSlJOpw2FmSbUoiumZmcGR+cXF5ZVTg8E0wAZTzeoQJmGWIgCe2tS0TdO0qW1SmwBwkBiLsixiLGIILAyAiYiZQcQMwM0M7g4iBJEQQoyRiLKq5ty0bdu0bZtGo9HOzvZoPLKULWfVHIMMpnpljICLcDeWcG/aeri7u3ZrbXtnZzKZNHWbLAHOoQDBHC8f+xKOnFl6gv6Hb8wuzHe7XWrNCA53Mmdg5ekNHHnxf7q1ef3a6vVrw+0tS21KTUrteDLa2dtzYGZmuj8Y9PrTRdmRGFgiS4ihKIpOUZRlKETECEwgUhKQgAgMMsDM3dXNAPABORCCHGAQWXZ1NVU3A5gEQQIxM8EcbprN3BxmJAAxMwEwdzeYw43ATERCDIdnbZu23h9ORqPxaL8a79fjEbQVltS2o/298aRKWWNZ/s39X8CRX/rhLy+cWJ5fuWv+9L3duTmwALT89DqOrD53EsCDP7p24UtL+EPG4/H2+sbzz7/w5S//2tmz50w1q1pWuIOojLHT6QKYjMdZNRZxfmHh9H0f+vgnHvvs5z734Q/f35vqsQQ3cwcRidABd1d1ACJ0AN8hx/sjvIvjHe6uqnAXYiICMB5Ndm5v3rh27fULF958482Lb761enN1d7jX1LUDamZqAFjkow8/9IWnv/CXfvsZ3HHzq0sgqJqZAuAQhBkfgBzv5YCZa96+vXXp7bdfP3/+1ZdfvXr18u7uXlO3zCTEIClC7HQ7QaK5CTNLASClNsZiqt/vTvW6vd6x+fmVlbsGs7PMbEBOyYEQRJg5Ctyatm3qpqqq1LYpJwASQhFjUXbKopAgwgKACCzCRDhi7nAcIohICCFKBOOAm+WcU9KU2vG42t3dnkzG2qbUNG1Twb2MMQhBjZjKGN29aerx/nDr9vbecK+aVE1bp6xgDiEQBwgNZmZO3X33L9/4izhy6YuznU6ZAhP87s+v4cjqswtwr/b29m5c37x+bfX6teH2lqU2pSaldjwZ7eztOTAzM90fDHr96aLsSAwskSXEUBRFpyjKMhQiYgQmECkJSEAEBhlg5u7qZgD4gBwIQQ4wiCy7upqqmwFMgiCBmJlgDjfNZm4OMxKAmJkAmLsbzOFGYCYiIYbDs7ZNW+8PJ6PReLRfjffr8QjaCktq29H+3nhSpayxLGePLfRn5rr96dm5hYUTy/Mrd82fvrc7NwcWgMC0/NQa7jj7D078dz+3t7tvMdDP/vjs1p79hZ/bG47tP/q+8NTju3/pFzf+6dm7wvilqav/bVa1rHAHURljp9MFMBmPs2os4vzCwun7PvTxTzz22c997sMfvr831WMJbuYOIhKhA+6u6gBE6AC+Q473R3gXxzvcXVXhLsREBGA8muzc3rxx7drrFy68+cabF998a/Xm6u5wr6lrB9TM1ACwyEcffugLT3/hU9/zPadO3z1zbO7UM5u448qvzwPgEIQZH4Ac7+WAmWvevr116e23Xz9//tWXX7169fLu7l5Tt8wkxCApQux0O0GiuQkzSwEgpTbGYqrf7071ur3esfn5lZW7BrOzzGxATsmBEESYOQrcmrZt6qaqqtS2KScAEkIRY1F2yqKQIMICgAgswkQ4Yu5wHCKISAghSgTjgJvlnFPSlNrxuNrd3Z5Mxtqm1DRtU8G9jDEIQY2YyhjdvWnq8f5w6/b23nCvmlRNW6esYA4hEAcIvTz3JRw5s/RE+RMvTs0MOp2Skpu7Q53gAqw8vYEjL/3l9a2bN9dXb+zvbLdNNRmPhvt7k8mobttYFHNzs/2ZQX96OhQlQAYCiDkURdkpyk7RjTGCQQRiAzsxiMAMgMzc3EzV1B0QIQkxBCmLglkAM/WUk5nDjZklBGaGQ1VzTtnU3YkkBGEmBgMwGBzm7A5zMIiI4dADTVuPR9V4XI9HmttILnAznYxHG5ubw73hpGokFj83+UUc+eIX/qGXJ37+pe+qMNXpxp/9czNbe/65P7uFI6vPnfxrX9z/X39t9OnvKr/0U8fwDsc7hnt7q9euP//8N37zq89dePNNBgOWk8IdREWMU1NTBIzHk6ZtUk4hFv2ZwUce/uj3/8APfPyxx+45fWowM1B1MwPATCJMRO4OwJyYQITviOOQ4RDjDxDexfGOnHNT13CUZRlY3HT91tqrr7z6+tlzF15//frqzeHe3nhSaZuS5pSSm4cYACTN9957zw/84A/+zAv/Ge64/uwiM6t51gwgxCjM+ADkeC8HzAHf29m9cfXa+XNnn3/+hYtvXdzevt3UbWQhsLnFohj0B1O9fn/Q73W6sSjUrKkbc5OiCCLEPD09OLG8PDMzE4sCRGpKzEVRRBEImWpq26ppqmrStm3OGUAMMRaxU5axKESEQTjAHIMwi5nhkOMIszCTsHAQYSYiAO5uDnekpt4f7U9G43pSVePRaH9YTcapaTS1OSeYC5Gatm3T1m3b1HXV1HXValI3Io5FEYJA4uzcsdMfOv33Lv/3OHLllwexKLQT3f3059dwZPXZRbhVw73hjZtbN2+ur97Y39lum2oyHg339yaTUd22sSjm5mb7M4P+9HQoSoAMBBBzKIqyU5SdohtjBIMIxAZ2YhCBGQCZubmZqqk7IEISYghSFgWzAGbqKSczhxszSwjMDIeq5pyyqbsTSQjCTAwGYDA4zNkd5mAQEcOhB5q2Ho+q8bgejzS3kVzgZjoZjzY2N4d7w0nVSCxm5xdnjh2bnZ3vzx2bHhw7trxy/L77pubnQQIQmJafWsMdf/KPdT/3ifILf7T7q/988tJbCY6nv6/7kbvD9/03G7/6528///w3/uJzn5t944cZDFhOCncQFTFOTU0RMB5PmrZJOYVY9GcGH3n4o9//Az/w8cceu+f0qcHMQNXNDAAziTARuTsAc2ICEb4jjkOGQ4w/QHgXxztyzk1dw1GWZWBx0/Vba6++8urrZ89deP3166s3h3t740mlbUqaU0puHmIAkDTfe+89P/CDP/jkk08++PBDC4uLK89s4o5LXzkGIMQozPgA5HgvB8wB39vZvXH12vlzZ59//oWLb13c3r7d1G1kIbC5xaIY9AdTvX5/0O91urEo1KypG3OToggixDw9PTixvDwzMxOLAkRqSsxFUUQRCJlqatuqaapq0rZtzhlADDEWsVOWsShEhEE4wByDMIuZ4ZDjCLMwk7BwEGEmIgDubg53pKbeH+1PRuN6UlXj0Wh/WE3GqWk0tTknmAuRmrZt09Zt29R11dR11WpSNyKORRGCQOLLx76EI2eWnuj95Eu9/lQsCkpufkCd3Jno1NPrOPLSX97YuXXr9sat/Z3tyWQ83NtZ31ifTEbM0uv35hcWZuZmpmdmYlGau6qnrAQOIRax6JadEKMwETOJETsYBDCDiACoumrKOZsZEcUjnU5XggiLmqa2VVVzMFMMkQjuSDm1bWuq7s4iZVGIiBsAA2BgM8DhDhiIBWbZ3Nq2rSZNNWmqCcP73ZJh1WSyt7u7tra2vbMzGU0g4efT38ORLz7z1V985Xv+6MP4T37kxD96pf/SWwrg7/9WhSOrz50E8OCPrl340hJ+n+Mduzs7V9++dOb55//x1/7J22+/LTEIM9ygUHhgKYsCQNvWdd3WbZOzIsjy3ac++T2fevyJxx977OPLKytFUQizqjGRRBFmAO5QdQAiRIR/ew4Y4DhEAOP3EN7F8Y6cclPXMI8xasqj/f0rl97++jeeP//qa5cuX9rZ3kmqUAPDzHNKAGIsMrSum+Xl5U9/5tN/7+KP445rv75ALA43UwAcgjDjA5DjvRyHzPd2d69dvnrh3NkzL5y5+Pal3Z3tuqoFAgYT96f68/Pzc7Nzg8Gg2+nGssiqo9GoaVsHVK1t27LXXVpaGszMlp0uixgshliWZSwiM1S1bdu6rqpJ1bStmhJRCKEoik5ZxlgQExObGxNLEGHBuxCBWUBgFiFiYWJhAhEBcPfcprpuJuNRNRoNh8O93Z3h7s7ezu5kPNKcYAp3PZCyWoa7Jq3aRk0BSJSi7MQiSijmFhbvvf/ev3P+z+LItV+ZlRC0E9399OfXcGT12eNwrYfDvRs3d27dur1xa39nezIZD/d21jfWJ5MRs/T6vfmFhZm5memZmViU5q7qKSuBQ4hFLLplJ8QoTMRMYsQOBgHMICIAqq6acs5mRkTxSKfTlSDCoqapbVXVHMwUQySCO1JObduaqruzSFkUIuIGwAAY2AxwuAMGYoFZNre2batJU02aasLwfrdkWDWZ7O3urq2tbe/sTEYTSOjPzM3NL84vLc3OL/T7c8eWV07cd9/U/DxIAALT8lNruGNpXr7xC4sxkBrGtTPw1X9VXb6l/8fXRr/xF1bPPP/8X/qt/2D+jR+RGIQZblAoPLCURQGgbeu6buu2yVkRZPnuU5/8nk89/sTjjz328eWVlaIohFnVmEiiCDMAd6g6ABEiwr89BwxwHCKA8XsI7+J4R065qWuYxxg15dH+/pVLb3/9G8+ff/W1S5cv7WzvJFWogWHmOSUAMRYZWtfN8vLypz/z6SeffPLxT31yeWV55ekN3HHl1+cBcAjCjA9AjvdyHDLf2929dvnqhXNnz7xw5uLbl3Z3tuuqFggYTNyf6s/Pz8/Nzg0Gg26nG8siq45Go6ZtHVC1tm3LXndpaWkwM1t2uixisBhiWZaxiMxQ1bZt67qqJlXTtmpKRCGEoig6ZRljQUxMbG5MLEGEBe9CBGYBgVmEiIWJhQlEBMDdc5vqupmMR9VoNBwO93Z3hrs7ezu7k/FIc4Ip3PVAymoZ7pq0ahs1BSBRirITiyiheGHwyzhyZumJmb96ruyUEgIlNzd3M3Iw4dTTGzjyyl+9vbO2tr25trezPRntb25u3rh+ZTKadKa6g9mZE0vH5xcXZuaOdbo9dcspt03KakLMEopYxiDMRIFDIBYADkCEQAzATdORnDIxhRDLstPplp2ywyLulto25+wGYoRYAHDTNqWmblJKxByCdMsuCblmM4AAkDlgrIZDDne4qWXNbd02tda1MA36PctpZ+v2gY319Z2d3cmkyopfCl/CkX/whX/4Z//ZD/3Gn7m1fP+9ndlj44aY6IEf28CR1edOAnjwR9cufGkJv8/xjt3t7Stvv33mGy/8k3/yTy9fvlQUZVGEGEsG2pQ0J83qagBUtdWcVFuz7lR/aeXEI4888n2f/eyjjzy8eOJEr9tNKRNRDEECw6HqagpAWEQIhEOOQ4RDjkOEQ45DhAMOGOA4RADj9xCOOP6Aw81V1XLWNm1vbV25cvXNC6+/+uqrVy5dun379ng8doObOkA4wMQERpt1XE0WF+Yf+8Qn/tHOX8Ed174yLzEYYKoAWISZ8QHI8V4OmAO+vXn7zTfefOP862fPnrt548Z4NKqqRnMKIfZ6vcWFxVOnTs3NzgURFokhNCkN9/ZGk0lKaVJVo/E4xuL4iRODmdlOrxeLCKaiKPpTU7EoAKil9kDTVJMq5ZRVmSjEQ2VZxhCJCYCbgyAizIIjzMTERMTCTARiuBGzsBCTMKtZatvcZs2pqZv94XB/uDfc29ne2FxfXxsN92EGV1NzqDtgDpiptal1gKOURVl2umW3lKKzuLB47wP3/40X/nMcufnrx4koR3bz00+t4cjqs8fhVu8P91dXd9bWtjfX9na2J6P9zc3NG9evTEaTzlR3MDtzYun4/OLCzNyxTrenbjnltklZTYhZQhHLGISZKHAIxALAAYgQiAG4aTqSUyamEGJZdjrdslN2WMTdUtvmnN1AjBALAG7aptTUTUqJmEOQbtklIddsBhAAMgeM1XDI4Q43tay5rdum1roWpkG/ZzntbN0+sLG+vrOzO5lUWRE65cz84ql77j1xcmXm2OL8yZXFez80dewYiAEC0/JTa7jj+Bw//4vHi0g48mM/sf3D39v5Y4+Xf/S/3vjyn79x5hsv/ORv//HFtz5fFGVRhBhLBtqUNCfN6moAVLXVnFRbs+5Uf2nlxCOPPK81BbAAACAASURBVPJ9n/3so488vHjiRK/bTSkTUQxBAsOh6moKQFhECIRDjkOEQ45DhEOOQ4QDDhjgOEQA4/cQjjj+gMPNVdVy1jZtb21duXL1zQuvv/rqq1cuXbp9+/Z4PHaDmzpAOMDEBEabdVxNFhfmH/vEJ5783u/97Pd//z33nF55eh13XP2NBQAswsz4AOR4LwfMAd/evP3mG2++cf71s2fP3bxxYzwaVVWjOYUQe73e4sLiqVOn5mbnggiLxBCalIZ7e6PJJKU0qarReBxjcfzEicHMbKfXi0UEU1EU/ampWBQA1FJ7oGmqSZVyyqpMFOKhsixjiMQEwM1BEBFmwRFmYmIiYmEmAjHciFlYiEmY1Sy1bW6z5tTUzf5wuD/cG+7tbG9srq+vjYb7MIOrqTnUHTAHzNTa1DrAUcqiLDvdsltK0fmd+Is4cmbpieM/c0lioAPJzQ+oE5yBU09v4Mj5v7E/3NjY2lzb2doa7u1urN+6dvXqeDQsOp3Z2Zml5aXFpRPzi8d7vZ4DOWlVN5oVgBCHEIWFGCSIUVhwgBm/T800Z1XNObt5iCHG2Ol0yrLsdDrmaOoqJyUhYWYRd+TUppTatlVzYWKRoigE7K4GMBHA5jCDqpmDwHC4m+ec22S5tZSi8KA/ldp649bq+vraxtr61u7ueFKp4kvll3Hkl55+7s/81p/4zR+/ffL0Xd25WbD82E/t/ouXWhxZfe4kgAd/dO3Cl5bw+xzv2N7auvTGm2eef+G3f/ufX7tytShjp+z0er0gnJK2bVvXtaUMgpplTXXSZGqMWBSn7/nQH/nMpx9//BMPPvTg4sICQBLkAAuZupuZO4iERYTwDnc3Vzjc4QABxMJEOEAOB8EAB0BEAOMPkOO9HIcM49F4e2PjyqXLr79+/q2Lb129dHnz9mY1mjRtC3P8HiLGATVvNU3qam527qOPfPRfpL+JO659ZZ4kgOFmOMBMREyE90OO93LAHPDNtY2zr732xrkLFy9e3NzYbJq2aeqmrkRkZnpmcXFxZWVlejAjRCAC0KY0Go0n1aSq6/Fksj8aBZFjCwuDmZmpqX4sSxaJRex1e7EoJJC7p7atjqhmc2fisihiEUOMIsLE5qaqRBzkEAAm4iDCB4SZQIwDbiAmgrAQwdSapklta1mbup5MxsO93Z3bG5sbG+u31uvxhBlMAAwOAtzcYDmlSVU5UHY7ZafT7XTKbqcou0vLyw8+/ND/+M//JI6sPrsEUA5w99OfX8OR1WePwz2NR+PNjeHGxtbm2s7W1nBvd2P91rWrV8ejYdHpzM7OLC0vLS6dmF883uv1HMhJq7rRrACEOIQoLMQgQYzCggPM+H1qpjmras7ZzUMMMcZOp1OWZafTMUdTVzkpCQkzi7gjpzal1LatmgsTixRFIWB3NYCJADaHGVTNHASGw90859wmy62lFIUH/anU1hu3VtfX1zbW1rd2d8eTShVSFLMLi6c/dP/Ju+5eOH5yfnllfuVUd24WLACBafmpNdzxg5/sPPPZ7uc/0/niP5tcWdNf/q3Jv/7549t7+n1/evPXf/zKmedf+J//nx9Zuvh0UYZO2en1ekE4JW3btq5rSxkENcua6qTJ1BixKE7f86E/8plPP/74Jx586MHFhQWAJMgBFjJ1NzN3EAmLCOEd7m6ucLjDAQKIhYlwgBwOggEOgIgAxh8gx3s5DhnGo/H2xsaVS5dff/38Wxffunrp8ubtzWo0adoW5vg9RIwDat5qmtTV3OzcRx/56JPf++k//iP/4f33fWjl6XXcce3ZRRxgJiImwvshx3s5YA745trG2ddee+PchYsXL25ubDZN2zR1U1ciMjM9s7i4uLKyMj2YESIQAWhTGo3Gk2pS1fV4MtkfjYLIsYWFwczM1FQ/liWLxCL2ur1YFBLI3VPbVkdUs7kzcVkUsYghRhFhYnNTVSIOcggAE3EQ4QPCTCDGATcQE0FYiGBqTdOktrWsTV1PJuPh3u7O7Y3NjY31W+v1eMIMJgAGBwFubrCc0qSqHCi7nbLT6XY6ZbdTlN3/m/42jpxZemL5b93AIaLWDQfcSZ3cTj2ziSNX/o6Ptre21te2Ntd3bt/eWF+7sXp9uLebTPv9/vLyyRMnTy4sHZ/uTxOTqTdNUlU4hFgokDCgYIRALCwEAxzm5mZqDiaYw039CBGFEMpOdzDdB9FoNDLzTlmEEACklCeTSjURMQhMwowDBLAcCiKA/H98wWmMrud5H/b/dV33fT/Lu8w7M2eZmXN4JIs8h4sVyA7k2qSFuMiCLjHF0EFTtEAXGGjafCjSIkDRD4GRLkj7xUhrw5adok2aNkAMR1JE1S0KN4FFi9vhoUxRokjK4nKWWc6Zfd7nfZZ7uzoz8okMMtDvF1NKMfsYNQMkBEChOWkIGiM0F0bqugx9t7O9tbO1dW9zc3//sO06Zf4d+7s494//2v/zG7e+8PzP4a/+xdlXXis+2sU//v3+cJ5xbuuFdQA3/t2dd//JGhP+hOJMxu7eg/e++/Ybr9968RvfuHv3rrW2LuvxeFQUJRFSTL7rvPchhCFGH4YhJK8pZU3A6ury9ceuf+6nPvf0M8889uhnptOpKwpmyllTCDnDWCEWZiIABKhq1hTTEEOKGVBhZjHWiDCDKKsqqUIBImEhAkA4p1BVAESEf0lxJuP+/Z233/z229/5zjvvvLO1uTmfN0PXhRByTDlngEUIipQ15eBj8tH7lFZmyzdu3Pgm/SYeuvPlC2AmJpzTrGAQMRPhE0jxcYozWXe2d779xrfeffudDz/86PDwEEAYYtc2AI0n4+n41KSq67IsmTn4EFPSnIfg+8H3fdcN3ohMppPRdDKeTIqqMmxYzljnqsoRkx+Gru+7rkspiYgxxjlnxLAIE0CUs+YUARhjxBgRMcYaOQMCE4GYCKcYlKE4l06F4Afft13wQ/Th8Ohg8+7d/Qf323mTUiysEyNGwEBKWVNKqot2cbB/kDSNJpOqKsXZoqzqenzt05/6s5///H/+u38B57a+ugYgGALwqWe3cW7zyxeJkIYhnRw3B/v793f2d+8f7u09uL9zb+vuyfFRyGk8Hm9srF9eX7+wdmkynhBTTjoMIaUEhRALGRIGEhjGEAsLIQOKrFlzTlnBhKzQnPQcERljirKaTsYgapomZy0LZ4wBEEJs2y6lQMQgMAkzThHAcsaIABJTSjH7GDUDJARAoTlpCBojNBdG6roMfbezvbWztXVvc3N//7DtOmUuR5PVi5c3PvXp9avX1jceWV1bn1y6VI6nMAbEYNp4bgcPvfz3L/2tXztSxWTEv/5fLv32P1v83sv9U5+2L77Z/fp/9N5b33r977/0ua7r1vf/u7qsx+NRUZRESDH5rvPehxCGGH0YhpC8ppQ1Aaury9cfu/65n/rc088889ijn5lOp64omClnTSHkDGOFWJiJABCgqllTTEMMKWZAhZnFWCPCDKKsqqQKBYiEhQgA4ZxCVQEQEf4lxZmM+/d33n7z229/5zvvvPPO1ubmfN4MXRdCyDHlnAEWIShS1pSDj8lH71NamS3fuHHj577wzC8+98Xrj9+48twOHrr7tcsANCsYRMxE+ARSfJziTNad7Z1vv/Gtd99+58MPPzo8PAQQhti1DUDjyXg6PjWp6rosS2YOPsSUNOch+H7wfd91gzcik+lkNJ2MJ5OiqgwbljPWuapyxOSHoev7rutSSiJijHHOGTEswgQQ5aw5RQDGGDFGRIyxRs6AwEQgJsIpBmUozqVTIfjB920X/BB9ODw62Lx7d//B/XbepBQL68SIETCQUtaUkuqiXRzsHyRNo8mkqkpxtiiruh7/c/1NnLu19vmNL93DORo0AyCAslJKj/zSLs5t/gPbHh7s3t/Z331wuLf74MH9nftbJ0eHnR+Ksrh0+fLFy5cuXro4nkzEWChSTDllZPApEhCATAwjzIasFUBDSCkF733OWUSI2BgBkLJqzgBc4abTKRM1TaOqVVUZY7Jq8L5tu5iStcaIMHPOOcQIwBqxYoxzAMeEFFMIISuYhJmRAU0pJspJACtUFs637YP721vbW1tb2weHh207QOT/yP8I5/7Jv///6ejRX/0X11jM0sT8+n+x9Nv/V//3fqfBuY++vOYs/Qf/7QEB/+hXVvBDijMZ9+/vvP3mt2/dvPnSSy/d29yyRkZVNR1Py6pyRjIQffDexxB77/uh9ylGzX0I/eBd6S6uXrzx+PWnn376yaee3FjfmM1m1jlhSikTkbFGmHGGiEmz5qzB+7Zrh2GIPgAwzhbWFUXpCksiICgyiIiZiBhnCGdUcYqIcEpVs6aUh2FomubOBx++cfPmd7/73Q/ef//w4AAZmrOeyhkAg0UoK2KIPgUffYgp5LQ8W370+qOv8G/jobtfuahExMREGdCUFGARJsInkOLjFJo1Z93e3Hrz1re+/8679zY35ycNCL7rjg6OU0rjybiuqqJwZVmVZQHiGAMUbAxUh+BDjD4EYalG9Wg8Gk3GRVmJscwMwBpTjWoiBD90Xd/3XUzJGGONsc4ZY5jOMHPOGlMkImuMGGuMWOusEWYBgYlATAQG4VyGas45phDj0PfdYjH0Q87p+Ohw686dw8OD6L2QjKqiKJwRJiDG5L0fhu74eL6/vxtSmkwnRVmQGFcWdT3+iUcffeYLP//L//DncG7zK5eyanIGwKef3ca5zS9fICL4oN2iPTzYvb+zv/vgcG/3wYP7O/e3To4OOz8UZXHp8uWLly9dvHRxPJmIsVCkmHLKyOBTJCAAmRhGmA1ZK4CGkFIK3vucs4gQsTECIGXVnAG4wk2nUyZqmkZVq6oyxmTV4H3bdjEla40RYeacc4gRgDVixRjnAI4JKaYQQlYwCTMjA5pSTJSTAFaoLJxv2wf3t7e2t7a2tg8OD9t2gEg9WVq5ePHSlavrV66tX7m2enltvLLi6jGchQhAG89t46GtF9bxpynOZNy/v/P2m9++dfPmSy+9dG9zyxoZVdV0PC2ryhnJQPTBex9D7L3vh96nGDX3IfSDd6W7uHrxxuPXn3766SefenJjfWM2m1nnhCmlTETGGmHGGSImzZqzBu/brh2GIfoAwDhbWFcUpSssiYCgyCAiZiJinCGcUcUpIsIpVc2aUh6GoWmaOx98+MbNm9/97nc/eP/9w4MDZGjOeipnAAwWoayIIfoUfPQhppDT8mz50euPPv3MM7/4/PNPPHFj47kdPLT5wloGNCUFWISJ8Amk+DiFZs1Ztze33rz1re+/8+69zc35SQOC77qjg+OU0ngyrquqKFxZVmVZgDjGAAUbA9Uh+BCjD0FYqlE9Go9Gk3FRVmIsMwOwxlSjmgjBD13X930XUzLGWGOsc8YYpjPMnLPGFInIGiPGGiPWOmuEWUBgIhATgUE4l6Gac44pxDj0fbdYDP2Qczo+Oty6c+fw8CB6LySjqigKZ4QJiDF574ehOz6e7+/vhpQm00lRFiTGlUVdj/+5/ibO3Vr7/MaX7qnmrEqdZgYIoKyS8pVfeoBzd/+BbY+O9h/sHO3uHh4dHuzv7u7eb06OB+/FyXRpabY8my7PRuNx6UoSgWZkQAGwEBGzEJhBAudMUZYghMF3fTdv5iEEI1IURVlVzloAOWfvPYtMxmMATdMqclVVxlhoDj52faeanXMsAiBnDd7nnITFGGOdZZacKWVNMQNMLEwEArIiJuQkABOE4PvuYH/3we6pB8fHJ13vIfIbB7+Gc7/zH/7B2vojK5cuTVZW3GgE4yCy8dw2zm29sI5PUpzJ2Nne+qPXb71+8+YrL7+8tbntrFSuHE0mdVk4VxgRgIEcU4ox9t4P0YeUuqGfN4ucU1lVa2vrjz9+/fqNG9ev37h65ZHV1eXxeGSNYxEQzqiCWIwAyDn3bXd0fDQ/OZnPmxiCGBnVo5WV5dF0WtUViygyFCDCOQIYBCYCQIRTqppy8jEEv7u794Mf/OD777733jvfu/PR7QcP7rdtZ1gI0JwBJiZDzEY0p6H3PoWQk0/JhzCbTR+7fv0V/m08tPnVSxkAExMBSJoBEDET4RNI8XGKFHMawtbm1ltvfvvD9z/Y3d9bzOc+xJPD463tTd/7pelkPB6PRiPnHJ1hAMYYVxYsklVTzilFYinKoqjLqh4VRWHFkgiBRKQoHZAH74d+6PouxQgiEXHWihhjjYgYESJKOTOdYmONNdYaY6wlImYGgYlATAQG4VyGJp+87/u2W7QL3w9h8IvF/HB/v1s0KQZn7WxpqSocE+WcU4qLRbO/v394fDw/OVFoUTpjnRIba1xZP3r9sT/3C7/wH/+vP4Nzd353VTWnqmTg089u49zWly+CoNHnRdseHe0/2Dna3T08OjzY393dvd+cHA/ei5Pp0tJseTZdno3G49KVJALNyIACYCEiZiEwgwTOmaIsQQiD7/pu3sxDCEakKIqyqpy1AHLO3nsWmYzHAJqmVeSqqoyx0Bx87PpONTvnWARAzhq8zzkJizHGOsssOVPKmmIGmFiYCARkRUzISQAmCMH33cH+7oPdUw+Oj0+63kOkGI2ny6url9curV9ZW39k5dKlycqKG41gHERA2PjiNh7aemEdf5riTMbO9tYfvX7r9Zs3X3n55a3NbWelcuVoMqnLwrnCiAAM5JhSjLH3fog+pNQN/bxZ5JzKqlpbW3/88evXb9y4fv3G1SuPrK4uj8cjaxyLgHBGFcRiBEDOuW+7o+Oj+cnJfN7EEMTIqB6trCyPptOqrlhEkaEAEc4RwCAwEQAinFLVlJOPIfjd3b0f/OAH33/3vffe+d6dj24/eHC/bTvDQoDmDDAxGWI2ojkNvfcphJx8Sj6E2Wz62PXrTz/zzC8+//wTT9zYeG4HD22+sAYgaQZAxEyETyDFxylSzGkIW5tbb7357Q/f/2B3f28xn/sQTw6Pt7Y3fe+XppPxeDwajZxzdIYBGGNcWbBIVk05pxSJpSiLoi6relQUhRVLIgQSkaJ0QB68H/qh67sUI4hExFkrYow1ImJEiCjlzHSKjTXWWGuMsZaImBkEJgIxERiEcxmafPK+79tu0S58P4TBLxbzw/39btGkGJy1s6WlqnBMlHNOKS4Wzf7+/uHx8fzkRKFF6Yx1SmyscWX9DfotnLu19vn1L91NMapm6jQzQABllaxXnr+Pc3f+N7M4OjzYfXBycHByfHJ8fHh0sNe085gSCcqyqOqqHo+ruq7KyjpLJAxkBSuYSIjZCDOEYJ2rRgUR+6GfL9rjw4NhGFi4KKvZZFpWFTFiSG23IHA9qgG0bauKsiqs2IycYhq8J0LpShHJmkOMfuhTVGPEWmOdE7GqyIqUAJCwgBinUkaOyFlISbNm7buuOTk6dXh02DSLPkQl+bs/+BWc+91ffvnS2sbs4qXJ8nJZj2ANWDae28a5rRfW8UmKMxn37t279cqrr7/26s3XX9/deVA4VxRFdco5a50YMSJEwoyc8+B9SDFo6rrhqDkeBk/g0Wh04eLKtUeuXb9x49q1a5cuXZzNlsejsbFGsxKBRYyx1hkwI2u7aA8O9vb3Dg4OD4Z+IKHpZLqxvn7h4qWl1eW6rkAEIGs+pSlnVQZABBCBFJpDjMH7fuja9vadO2++8a133/v+1r27+3sH/WIRU3LOETNyZrAYYWIRiin1fe9DykghpdYPS9PJY9evv8K/jYc2v3o5Q8HERACSZgBEzET4BFJ8nCLFlPqweW/7zT/61ocffXRydLRYLIbe7z7Y/fDD95vFYjKeLi9NZ8srdV0ZEWYhgrGurErrHDNnaMoJRGKMKVxZls45y5ZEiElErBFAgw+DH7q+997nnInIGGONsc5ZY62zwpxVmc6wGGuNNVaMCAsRAOIzBGIiMChDVZFDGPqhW3Rt27Rd5/u+79pu0YRhyClVhVtdWS7LUlPOKYYUF4tmf2/v8OioaZohepYzxjpXVqPx+NEb1//cL/zCv/M/P4lzd/7pqqqmsmDg089u49zWVy4BUD+kdrE4OjzYfXBycHByfHJ8fHh0sNe085gSCcqyqOqqHo+ruq7KyjpLJAxkBSuYSIjZCDOEYJ2rRgUR+6GfL9rjw4NhGFi4KKvZZFpWFTFiSG23IHA9qgG0bauKsiqs2IycYhq8J0LpShHJmkOMfuhTVGPEWmOdE7GqyIqUAJCwgBinUkaOyFlISbNm7buuOTk6dXh02DSLPkQlsWU1mi7NVi8sX1y7tLYxu3hpsrxc1iNYAxYQNr64jYe2XljHn6Y4k3Hv3r1br7z6+muv3nz99d2dB4VzRVFUp5yz1okRI0IkzMg5D96HFIOmrhuOmuNh8AQejUYXLq5ce+Ta9Rs3rl27dunSxdlseTwaG2s0KxFYxBhrnQEzsraL9uBgb3/v4ODwYOgHEppOphvr6xcuXlpaXa7rCkQAsuZTmnJWZQBEABFIoTnEGLzvh65tb9+58+Yb33r3ve9v3bu7v3fQLxYxJeccMSNnBosRJhahmFLf9z6kjBRSav2wNJ08dv3608/8/Bef/ys3nrix8dwOHtp8YQ1A0gyAiJkIn0CKj1OkmFIfNu9tv/lH3/rwo49Ojo4Wi8XQ+90Hux9++H6zWEzG0+Wl6Wx5pa4rI8IsRDDWlVVpnWPmDE05gUiMMYUry9I5Z9mSCDGJiDUCaPBh8EPX9977nDMRGWOsMdY5a6x1VpizKtMZFmOtscaKEWEhAkB8hkBMBAZlqCpyCEM/dIuubZu263zf913bLZowDDmlqnCrK8tlWWrKOcWQ4mLR7O/tHR4dNU0zRM9yxljnymo0Hv/f7a/i3K21z69/6W5KUVWp1UyAAKSQpFee38G5D/4Xbo+ODvd2m+Pjrm0Wzfzk5Kj3HRNlqEJF2JSuKMtRXZdlaY0FEbJCIQRhMdYwM5CcdXVdEdEwDPOT5uBwr+06IqrKarY8q6uKWULw83mjQF3XIHSLTqFVUYi1AHJOp0SkKisRSSkNvu+6NqXsCudcWThnxCg4K7JCFUSGwKpZc8oxsmYmRc4x+NAPbbvo2lOLbhiGkDL4b736N3Dun/2Nb61eWJuurk6WZ2VdwxgIb3xxG+e2vr6OH1L8iOJMxt07t19+8Q9v3Xz9zW+/eXRwWJdVWZbWGiuCDDAbETFijWGwTyGpKqHzfbNo+sHnmBSZiafT6ZWrVy9fujRbni3PZtOlmStK1WStGY+mZVWVhRMjULRdd7i/t7u/t/tgr+06IsyWlq5evfbItWsbV68sry4XZQmi4EMIfhiGHGPOGURMDEBT8v3QNM2iaebz+Yfvf/DKq69++MEHzXzuvWdiK8ZZSyJISkwizCzMHGNo2zamlAGffNt2k8n0+o3HXqLfwkObX1vLUABMBEJSBUBEDMInkOLjFJo1hXzvzr3XX33t/fffn89P2rbz3j/YefC9d94+Pjgu62p1eXnj6pULFy5MxmNrbYqRREZ1bYvCWpMJ6ZSqAixsnXPWirEiQkQibMUCGmII3nddP/gheJ9zZmZjbVmURVFYZ0WEiAAwkZwy1hg5xSxEICImJiIWZiKcy6opJN/7vuua5mSxWHTNIvg+xZBjSinURbG6ulIWRRh8iCGlMAxD17ZHJ8f7BwfNohlCMGKns6XZ8sryhZUbjz/x81/4wr/+ty/g3OZXL2fAGybgM89u49zWVy6DkIYhzE/ao6PDvd3m+Lhrm0UzPzk56n3HRBmqUBE2pSvKclTXZVlaY0GErFAIQViMNcwMJGddXVdENAzD/KQ5ONxru46IqrKaLc/qqmKWEPx83ihQ1zUI3aJTaFUUYi2AnNMpEanKSkRSSoPvu65NKbvCOVcWzhkxCs6KrFAFkSGwataccoysmUmRcww+9EPbLrr21KIbhiGkDGZXFNVoPJtNVy6uXlibrq5OlmdlXcMYCAPY+OI2Htr6+joUP6I4k3H3zu2XX/zDWzdff/Pbbx4dHNZlVZaltcaKIAPMRkSMWGMY7FNIqkrofN8smn7wOSZFZuLpdHrl6tXLly7NlmfLs9l0aeaKUjVZa8ajaVlVZeHECBRt1x3u7+3u7+0+2Gu7jgizpaWrV689cu3axtUry6vLRVmCKPgQgh+GIceYcwYREwPQlHw/NE2zaJr5fP7h+x+88uqrH37wQTOfe++Z2Ipx1pIIkhKTCDMLM8cY2raNKWXAJ9+23WQyvX7jsad//ud/8bnnbjxxY+O5HTy0+cIaCEkVABExCJ9Aio9TaNYU8r07915/9bX3339/Pj9p2857/2Dnwffeefv44Lisq9Xl5Y2rVy5cuDAZj621KUYSGdW1LQprTSakU6oKsLB1zlkrxooIEYmwFQtoiCF433X94Ifgfc6ZmY21ZVEWRWGdFREiAsBEcspYY+QUsxCBiJiYiFiYiXAuq6aQfO/7rmuak8Vi0TWL4PsUQ44ppVAXxerqSlkUYfAhhpTCMAxd2x6dHO8fHDSLZgjBiJ3OlmbLK8sXVv73D/9rnLu19vmNL91TaAao0wxAAFJI0ivP7+DcH/8WmqPD4/29tpmHoe/bdtE1MUZbGCAPweecIVQ4V9Z1VZbWOmHSpFAwwYhYY0Q4azbG1nXFBB/ComkODg+7rlPVoihm01lVV8QSvW8WTcqpKqqM3HcDkF1RWiN4yBpX1ZWIhODbvlssGs2oqsIVZekci1MiKEfNqkwgVVZNmlIOiZGZgRzj4IP3wfvg+yEEH2OIOYN++ff+Gs793t/87mzl4nR5dTSblXUFEQhtfHEb57a+vo4fUvyI4kzGndsfvfgH33jjtdfeeuutg4OjyaiuyqoqC2utsDCJtWKMERFV9adSSjkNoW+7fvBDisl73/WdFbO0PBuPJ3VVTqaTpdlyXVbMKMpyOp2NR3VRFGItEw3DcHx8dHR4uLd3sFi0YCxNJhsbV69ee+SRa9dWL16sxyNm6rq+KqXA1AAAIABJREFUb9u264bBpxigSkRQjSENXXd8ctKcnBzPT+7dufedt76zvbOtKbHIqKxcccoZlqzKRCJCLEwIPrZ9G2JMmgfv54tmMp08fuPGi/k38NDmC2tZFQATgSirAmAi/KuQ4uMUKWn26e6du6++/Mr7H7zfNou+H1KMO/cffO/tt/f29ozI8vLyp659an1j7cLqalFVMUZmrqqqcE6cBSHEFFNKmkBkrTXGWGNJGCBjuLAFgBBD8H7o+67vu65LMYLIWlvXdVWWxloRoXMAhNkYIyLMQkx8CkTCwkJEzAwg5wxojCkNoe+GeXPSNqfmwQ9QRU4xhMLZldnMWeP7wQefYojpTDNvdvd3j07mbd8Z51ZWVy5fXtu4euXGk0/+zL/2sz/zNx3Obb2wDqAnnPrMs9s4t/WVNUDj0A/Hx83R4fH+XtvMw9D3bbvomhijLQyQh+BzzhAqnCvruipLa50waVIomGBErDEinDUbY+u6YoIPYdE0B4eHXdepalEUs+msqitiid43iyblVBVVRu67AciuKK0RPGSNq+pKRELwbd8tFo1mVFXhirJ0jsUpEZSjZlUmkCqrJk0ph8TIzECOcfDB++B98P0Qgo8xxJxBEGuKshiNx0vLs5WL0+XV0WxW1hVEIATCxrPbeGjr6+tQ/IjiTMad2x+9+AffeOO11956662Dg6PJqK7KqioLa62wMIm1YowREVX1p1JKOQ2hb7t+8EOKyXvf9Z0Vs7Q8G48ndVVOppOl2XJdVswoynI6nY1HdVEUYi0TDcNwfHx0dHi4t3ewWLRgLE0mGxtXr1575JFr11YvXqzHI2bqur5v27brhsGnGKBKRFCNIQ1dd3xy0pycHM9P7t259523vrO9s60psciorFxxyhmWrMpEIkIsTAg+tn0bYkyaB+/ni2YynTx+48bPPvP0L37xi9cfv7Hx3A4e2nxhDURZFQAT4V+FFB+nSEmzT3fv3H315Vfe/+D9tln0/ZBi3Ln/4Htvv723t2dElpeXP3XtU+sbaxdWV4uqijEyc1VVhXPiLAghpphS0gQia60xxhpLwgAZw4UtAIQYgvdD33d933VdihFE1tq6rquyNNaKCJ0DIMzGGBFhFmLiUyASFhYiYmYAOWdAY0xpCH03zJuTtjk1D36AKnKKIRTOrsxmzhrfDz74FENMZ5p5s7u/e3Qyb/vOOLeyunL58trG1Sv/07f+Os7dWvv8+pfukRAAGjQDYIAUnHDl+W2ce/c30vzw8PjwYFg0Kfrofe97QMuqAGnn+xRjQjZGXFEWzllrmBg5I4PBYsRaMSwAjBHnHAvlpMPQN8286/qUVERGo6ooHLHklBbtIqfEYnNKw9Bl1cIVYgwRGCxGiqKoRyNmGgbfLdpm0eScRqO6KEvrnIjJEFXkrFkJYFVoSiknhATACCHF6H1KPsekmmLKMWvSnJT/6v/5F3Hu//2v3p/MViZLs9F0WtQVGQbTxrPbOLf59XXCn6L4E4pTdz66/c1vvPjayy+/ceuNvd0HVT2ajMbLs6XpZDodj6uqss4Js6bcDf28OdMNbTcMPgwpJc0IMXRtl3IyRogEmlxRTifT0XhcFq4e1ePxuCor66wxRoxJMQ9Dt1gsTk7m/dDnrFVVra6uXrp06fLa5eWVlfF4CYSmXTSn5vO2bb33KSZAc8rJh67vm2bRnDs5Ot7f3x/6jkSqwo2qUV3XRVGwCDQDEBYWBijH2Hb9EAYfQ9v2x83xeDx94snH/yD8Gh7afGEtqwJgOqP4cUjxcYoYYtcOdz66ffO112/fvj30XQxRs+7t7b337nu7uw9iTOPx+JErVzeurK+vrY0mEwDCbJyz1pCIAjHHENIpMMSeMsZYFoaqiBTOMUmMIXgfvO+6vlk0IQQAhXN1XRdFaawRYjCdAkBEwsIizCR8SojJsJCwEQEIUFXElHJKKebQD/Nm3jZN2zSDH6CaUwyDt4y6rg1T8D744KOHghld1+/t758smm7ojbUrq6vrV6985tHHnnzqqc/+1Od++j/DD21+bY2YPM58+tltnNv6yho0x75rj47mh4fHhwfDoknRR+973wNaVgVIO9+nGBOyMeKKsnDOWsPEyBkZDBYj1ophAWCMOOdYKCcdhr5p5l3Xp6QiMhpVReGIJae0aBc5JRabUxqGLqsWrhBjiMBgMVIURT0aMdMw+G7RNosm5zQa1UVZWudETIaoImfNSgCrQlNKOSEkAEYIKUbvU/I5JtUUU45Zk+aknEjIOFdV5XhpMluZLM1G02lRV2QYTApceXYbD219fR0/pPgTilN3Prr9zW+8+NrLL79x64293QdVPZqMxsuzpelkOh2Pq6qyzgmzptwN/bw50w1tNww+DCklzQgxdG2XcjJGiASaXFFOJ9PReFwWrh7V4/G4KivrrDFGjEkxD0O3WCxOTub90OesVVWtrq5eunTp8trl5ZWV8XgJhKZdNKfm87ZtvfcpJkBzysmHru+bZtGcOzk63t/fH/qORKrCjapRXddFUbAINAMQFhYGKMfYdv0QBh9D2/bHzfF4PH3iyceffubpf/Mv/+Xrj9/YeG4HD22+sEZEih+HFB+niCF27XDno9s3X3v99u3bQ9/FEDXr3t7ee+++t7v7IMY0Ho8fuXJ148r6+traaDIBIMzGOWsNiSgQcwwhnQJD7CljjGVhqIpI4RyTxBiC98H7ruubRRNCAFA4V9d1UZTGGiEG0ykARCQsLMJMwqeEmAwLCRsRgABVRUwpp5RiDv0wb+Zt07RNM/gBqjnFMHjLqOvaMAXvgw8+eiiY0XX93v7+yaLpht5Yu7K6un71ymcefezvvvjv4dyttc9f+tJdYSImGjQDYAAKSbjy/DbOvfPrcX50dHxw4PsFaUoxhRSF4UoHRgg+xJCQABhrDQsJCTEpSCEEYREjQgSAha11RgREOcWu6/q+DyGCUBSFs5ZFcsp934UYiSTFMPQ9oMaV1ggIhsW5oqzKUV2Dueu6tu0WzUlSHVVVURbGWhIBJCulrAABAtWccs5JUxJARKAphSGHBGRAFciKBESlf+tLP4tz/+Jv3x1NZqPpUjWduLJgw2C68uw2zm1+fR0A4SHFjyju3L790osvvvbSK6/dvLl7f3dc19Pp9OLFCyvLKytLS9VoVBgLwMfQt+3JfH7SnDTz+aLvfPAphZhyCtH7PoQYQjoVU7RiRqNxPanHo3pUj8uqKpwTI6esGDDnlELwbd9H72OK1ph6NJ6ems2m0+loPAF40S6aedM083bR9UMfY0w5aUohJD/0bdf3Xdu1vQ9eUwKzFVNW5WQ0quu6KktjDQAiZiYQkyKlNHjfdV07dIvF4vj4ZLI0ffKpJ3+//VU8tPnCWlYFwHRG8eOQ4uMUfvDzk+bO7btvvfnWvc17Xd8H73NIhwcHH96+fbC7570vq2r90uW19bW19bXpdGpESIRPCYEFQNR0KqYMJmetdVaMYaKYEhEV1hGxpngmxH7om6aJIQCwzo1Go6IomFmIwYRzdE74TxCLEREjp4wIs+ScVTWGELNqSMPg20XTtW3XdcEPOacUQvSeNRsRgqaUYgwh+BhjztkPvh26IcScE1tTj0Zr6+vXn3jiiaeeevInf/Jzfz3h3N2vrTFRIJz69LPbOLf1lTVoDm3bHh3Nj46ODw58vyBNKaaQojBc6cAIwYcYEhIAY61hISEhJgUphCAsYkSIALCwtc6IgCin2HVd3/chRBCKonDWskhOue+7ECORpBiGvgfUuNIaAcGwOFeUVTmqazB3Xde23aI5SaqjqirKwlhLIoBkpZQVIECgmlPOOWlKAogINKUw5JCADKgCWZGAqJTBYMOuKkaj0WQ2mi5V04krCzYMJgBXnt3GQ1tfX8cPKX5Ecef27ZdefPG1l1557ebN3fu747qeTqcXL15YWV5ZWVqqRqPCWAA+hr5tT+bzk+akmc8XfeeDTynElFOI3vchxBDSqZiiFTMajetJPR7Vo3pcVlXhnBg5ZcWAOacUgm/7PnofU7TG1KPx9NRsNp1OR+MJwIt20cybppm3i64f+hhjyklTCiH5oW+7vu/aru198JoSmK2Ysiono1Fd11VZGmsAEDEzgZgUKaXB+67r2qFbLBbHxyeTpemTTz359M89/Zf+7X/j0Ucf23j+Ph7afGGNiBQ/Dik+TuEHPz9p7ty++9abb93bvNf1ffA+h3R4cPDh7dsHu3ve+7Kq1i9dXltfW1tfm06nRoRE+JQQWABETadiymBy1lpnxRgmiikRUWEdEWuKZ0Lsh75pmhgCAOvcaDQqioKZhRhMOEfnhP8EsRgRMXLKiDBLzllVYwgxq4Y0DL5dNF3bdl0X/JBzSiFE71mzESFoSinGEIKPMeac/eDboRtCzDmxNfVotLa+fv2JJ/7O7/8Szt1a+/ylL90lJiaiTjMDBJCCFI88t41z3/v11BwdHh8dhr5jBrKqJhCsExYkzSknhWZNOWdVxTkBmMgQCwkTEZBUhcg4a62zRlTRD70fhq7voWqts0bEuJST7/uYE4AYkvc9oNY6EWFmI6asqrKqRnUFoOu6ZtG2iyZrLorSOidWiEVVVJFxSpgFiqxZs1JOQmRFCJqjzzEBWQEwK5AyJfCf/3ufxblv/DcP6vG0Gk+qydiWjoSY6cqz2zh39+vrjDOEc4ofUdy9c+flP/zmay+/cuvmzb3dvclkPJvNLl24uLy8Mp2MnXNEnDWnEIdTfdu0i5P5cdu0ne+Hvm/7LoWYNeeUfIiaM0DWmsIV1agaTyZ1VRnjRIQBEjIiLCLMGUgpxZBS8qoQEWucK1xZVXU9BlHbD13bte2i77phGHxIOadTmlKI4VQMMaaEnFnEGCGSoizqqqrrejQaFYUTEWYBgQBk5JR8CO3Qzefzk/nJybyZLi39mc9+9veO/wc8tPm1tQwFwHRG8eOQ4uMUXdcf7B9u39v+4x98sLOz07aLtlmcOjo8erC728ybHGJRFiuz2fLqysULF8bTSeEcESdkQFkYRFmRoYCyiC0Ka60Y0ZQH76FZyBCgmjVnACGExWIRQmAi69x4NC6cy1AGkTAAVQVA55hOsTEixlhrjTHWWibKqjnlmGIKKfgw9EPf9X4YUowh+Bh8CiHHmGNMMUAzGDlr8H27aI9OjlNMRVEVdVFUJZi8j8srK9efeOKJn3zq8Sef+On/NOPcna+tA4gMBj797DbObX51jbKGtlscHTZHh8dHh6HvmIGsqgkE64QFSXPKSaFZU85ZVXFOACYyxELCRAQkVSEyzlrrrBFV9EPvh6Hre6ha66wRMS7l5Ps+5gQghuR9D6i1TkSY2Ygpq6qsqlFdAei6rlm07aLJmouitM6JFWJRFVVknBJmgSJr1qyUkxBZEYLm6HNMQFYAzAqkTAmcQSqWrLNlXY+n1XhSTca2dCTETBl45NltPLT59XXCOcWPKO7eufPyH37ztZdfuXXz5t7u3mQyns1mly5cXF5emU7GzjkizppTiMOpvm3axcn8uG3azvdD37d9l0LMmnNKPkTNGSBrTeGKalSNJ5O6qoxxIsIACRkRFhHmDKSUYkgpeVWIiDXOFa6sqroeg6jth67t2nbRd90wDD6knNMpTSnEcCqGGFNCzixijBBJURZ1VdV1PRqNisKJCLOAQAAycko+hHbo5vP5yfzkZN5Ml5b+zGc/+3PPPP3n/9Jf+Imf+MzG8/fx0OYLa0Sk+HFI8XGKrusP9g+3723/8Q8+2NnZadtF2yxOHR0ePdjdbeZNDrEoi5XZbHl15eKFC+PppHCOiBMyoCwMoqzIUEBZxBaFtVaMaMqD99AsZAhQzZozgBDCYrEIITCRdW48GhfOZSiDSBiAqgKgc0yn2BgRY6y1xhhrLRNl1ZxyTDGFFHwY+qHvej8MKcYQfAw+hZBjzDGmGKAZjJw1+L5dtEcnxymmoqiKuiiqEkzex+WVletPPPF3fv+v4Nyttc9f/tI9ZQJAnWYGCCAFKR55bhvn3vkNnR8enRwfBj9YBjOQM1jZCDFYCKRZc0gxeB9i0JgIKizCYoiYGAADWcFE9oyzzkGz90PX9W3bppSts84aMVZz7ochp0REMSbvh6xqjWERI+Kcqx5SzYu2a9u26zrVbAtnrWURkGQlBTIYJMLCkKwJOSOrMJwxBNWUkBOQASixgjI4g77wP17HuZf++6NyPC5G42JUu9KSEDE98uw2zt35+joBjDMEQPEjirt37rz8zW/eevW1W7feONzfn06ny7Pl2fJsPBo5MQBCjJozFGdIQ/Ddomn7ruu7xWIxP2l88AA0p8F7AO6UNWJcWbp6NCqL0hjDxFkTiAzLKWMtETE4awohpJSyKgAmEmuLogLJMPjB+6HvBz/4IYToQ0w5pRhCVs0pqeasMCLOOhYhhjW2KMtRXY1H46qurHPMAkA1a0oxppRT23Un81PNfD5fWV3+6Z/+s/9051fw0ObX1nJWMJjOKH4cUnycYrHoDvb2N7fu3/3ozv0Hu/P5vJmfHB+fNPP5YtH0/aApWevGk/HS0tLK8vJ4Mi6cBTDEkHNmYTCzCIhAEDFFVYgxQhxDaLs2hoAMqCIrC1tjU05d18UYiagsitF4bIxJKQEQESLSc0SEc8JMxMYa55y11lnHIgDyqZSC9103+H4Yep9TRNaYYvBDigE5xxCGrtWUxErOqe+Ho6PD+/cfpJxms5Xlldl0eQbh+Xw+WVp64qmnHn/iiUevP/a5/yTi3J2vrQOIDAY+/ew2zm1+dZ1UQ9e1R0fzw6OT48PgB8tgBnIGKxshBguBNGsOKQbvQwwaE0GFRVgMERMDYCArmMiecdY5aPZ+6Lq+bduUsnXWWSPGas79MOSUiCjG5P2QVa0x/P/zBTcxmmbXfdj/55x77/M87/vWZ09PV/V88gsySZMU5REphYFlZRNrMSQIIxJhLeKsAmSRbOIYMZIgi2ShxHYWSazYTgwDQTZZaDwc7yIYsExTnOEIomnqyxRnpnumq7qru7qq3o/n4957zslbRTUESAZ/P5EgklLqnnG3TT/0fT8Mg7vFJsUYWQQk5uSAgUEiLAwxV5jBXBgpBIK7KkwBA+DEDjKwgRTsLBRSSLN2sWjmi2Y+S20kIWJy4OXXT/HMg7eOARAAx59yfHj//re/9a13v/P2u+/+zsX5+e7u7sH+wf7B/mI+TxIAlFrdDI5r5KXkYbPux2EYh81ms1quc8kA3HTKGUDaikFCats0m8/bpg0hMLG5giiwbIUYiYjB5lpKUVVzB8BEEmPTdCCZpjzlPI3jlKc8lVJzqWqqtRRzN1V3M0cQSTGxCDFiiE3bzmfdYr7oZl1MiVkAuJur1qpq2g/DcrW1Xq1Wh7cOvvjFn/nSz3353/8rv/Dqx169+7WHeObBN4+IyPGTkOPPcmw2w9Mn5w9OHn34wf1HZ49Xq9V6tby6Wq5Xq81mPY6Tq8aYFjuLvb29w4ODxc6iSRHAVIuZsTCYWQREIIiEpmskBCGupfRDX0uBAe4wZ+EYopoOw1BrJaK2aeaLRQhBVQGICBH5DSLCDWEm4hBDSinGmGJiEQC2pVpyHoYpj9M0ZtMK86q15ElrgVktZRp6V5UoZjqO0+XlxaNHZ2q6v394cLi/e7AP4dVqtbO39xc+85n/+pt/FTfePXrt+B8+UMcWDW4MEEAOUrz09VPc+MP/g/ury9XVVcljFBYmwB0KAgeKMUIAQ9Y89JtxmrRkAqKIcIjCDII5gcAsLDGGeC2Z6TiOm02/Xq+rapNSiiGmZOabzcbdQxB3TOOobiIhyLWmSd1sNuu6pmnNtd8MwzjknAHEJoUYiNlBauyAAUxBJBJETWEGU2GOQZjApnCDuwEgcrATFPLv/Q+v4sa/+rVV2y2a2ayZddLEEAhML79+ihv33zoGQADjGuGG48c+vHf/29/61rvvvPP9f/39q4urg/29xXzRzjp2DOtN3/ebYYBZ187m89ls3sUopeSiueS82awvnl6Ow1BNtdScM7EsFrOmbYUlSEhtjCk1sWEic8UWSWChQEIizAaYqamqGRzmzizCwcHFtBYtNdeyVXMpU86lFK1b6jAAzBJFYkwkDDcWSamZdd3OzqKbzbq2lRDcvKrWnKteG6ex3/SbfrPuh9vPP/faa6/9Px/8LTzz4M0jMweD6ZrjJyHHn+XY9MP546cnJw/vfXD/7Ozs8upquVz2q9U0jNVdq2otUaSbzXd2Frdu3ZrPZyCY6ZSzmpFwCEFiDEEA4igxNcLk7uM0rVarqe9rrq5KRDHG+XzORNOUzZSZY0qL2ZyDlFIAhBCEWc3wjLsTkTCLSNxKqUkphEDMALl5mfJqvRn7qdZiZgyYmZZsqgEotQybddWagpRaN5v1k/MnJw9OspZbh7eee/7283fuSIrr9WZ3f/+zn/vcJz71yZdeeekz//GAG/e/eQygEhh49fVT3HjwxjEAHcf+6qq/ulxdXZU8RmFhAtyhIHCgGCMEMGTNQ78Zp0lLJiCKCIcozCCYEwjMwhJjiNeSmY7juNn06/W6qjYppRhiSma+2WzcPQRxxzSO6iYSglxrmtTNZrOua5rWXPvNMIxDzhlAbFKIgZgdpMYOGMAURCJB1BRmMBXmGIQJbAo3uBsAIgc7QSFG5BwhiZum7RbNbNbMOmliCAQmAC+/fopnPnzrmHGNcMPxYx/eu//tb33r3Xfe+f6//v7VxdXB/t5ivmhnHTuG9abv+80wwKxrZ/P5bDbvYpRSctFcct5s1hdPL8dhqKZaas6ZWBaLWdO2whIkpDbGlJrYMJG5YosksFAgIRFmA8zUVNUMDnNnFuHg4GJai5aaa9mquZQp51KK1i11GABmiSIxJhKGG4uk1My6bmdn0c1mXdtKCG5eVWvOVa+N09hv+k2/WffD7eefe+211770c1/++a985cVXXr771VM88+CbR0Tk+EnI8Wc5Nv1w/vjpycnDex/cPzs7u7y6Wi6X/Wo1DWN116paSxTpZvOdncWtW7fm8xkIZjrlrGYkHEKQGEMQgDhKTI0wufs4TavVaur7mqurElGMcT6fM9E0ZTNl5pjSYjbnIKUUACEEYVYzPOPuRCTMIhK3UmpSCiEQM0BuXqa8Wm/Gfqq1mBkDZqYlm2oASi3DZl21piCl1s1m/eT8ycmDk6zl1uGt556//fydO5Lier3Z3d//7Oc+95/93z+HG+8evXb8jx6oY4sGNwYIIAcUL3/9FDd++I9Cv1yuLi9rzjEQEQB3KAgUqEmRhcx9zLnfrIdxqCUz0IQQOcYgTDADAyxBeEu2Qkxwm6ap7zfL1apmjSnEdM3Mh37j5jEFdx+HrF6FggQJQZqmmc3mbdukGNVs0/d5ytWUmVKTKEQQ4KgGd4CEWIIkgNzcTU2VCVGEGQEgh8OwRewEAznxl/77l3Dj2/9z37Sz0HVN18UmhkjE9PLrp7hx/61j3CCAAcINx499dP/D3/7Wt9/97jvf+93vXT59upjPm6ZLUWrR9Xo5bIZxHJl4Z2exu7W306aopqq5qo59f3lxNQx9rjnnUnIJIoudedO2TMIiQZiDRAlE7FAABGGARViY6ZqTmwMGOMwcTCBxoKqZuqqaai4l1zxNJZdcc66qMIAR5FqMiRhuIOEooW3bxe5iPpvPZl0IQc1rzlPOpeSqmnMet6ZxmPLRnaOf//kv/59/+F/gmQdvHpkZmJiuOX4ScvxZjs2mPzs7/+jB6b337589frReb4ahL+NkaiKsZnma4EgpzWbzw/39ZtYCMK3DNKqbhBBDjG0KIQCgICFEENxsGIbLi8t+vR77QWsV5lnb7e3vxxS1VjMDEGKctR2YSs4AYkpBxPwabrg7zLHFFEMIMaaUYowpJmYhopLrer2ZxqnW6mpwh5m6ChCFSyl9v6klMzCNw/nlxeOzxyenJ7Xo/sHe7Tt3jl447mbdlMvBc7c++7nPf+wTHz86vvNTv7rCjftvHQOoAAOvvn6KGx++ccyATtO0WvbL5erysuYcAxEBcIeCQIGaFFnI3Mec+816GIdaMgNNCJFjDMIEMzDAEoS3ZCvEBLdpmvp+s1ytataYQkzXzHzoN24eU3D3ccjqVShIkBCkaZrZbN62TYpRzTZ9n6dcTZkpNYlCBAGOanAHSIglSALIzd3UVJkQRZgRAHI4DFvETjCQE1eIszgnbpqmnYWua7ouNjFEIiYAL79+imfuv3VMAAOEG44f++j+h7/9rW+/+913vve737t8+nQxnzdNl6LUouv1ctgM4zgy8c7OYndrb6dNUU1Vc1Ud+/7y4moY+lxzzqXkEkQWO/OmbZmERYIwB4kSiNihAAjCAIuwMNM1JzcHDHCYOZhA4kBVM3VVNdVcSq55mkouueZcVWEAI8i1GBMx3EDCUULbtovdxXw2n826EIKa15ynnEvJVTXnPG5N4zDloztHP//zX37tS1/+Sz/3pRdefOHuV0/xzINvHhGR4ychx5/l2Gz6s7Pzjx6c3nv//tnjR+v1Zhj6Mk6mJsJqlqcJjpTSbDY/3N9vZi0A0zpMo7pJCDHE2KYQAgAKEkIEwc2GYbi8uOzX67EftFZhnrXd3v5+TFFrNTMAIcZZ24Gp5AwgphREzK/hhrvDHFtMMYQQY0opxphiYhYiKrmu15tpnGqtrgZ3mKmrAFG4lNL3m1oyA9M4nF9ePD57fHJ6UovuH+zdvnPn6IXjbtZNuRw8d+uzn/v83/j1v4gb7x699vw//AhEAGhwAyAAOaB4+eunuPGjf9wMy+Xy8lKnkYWFYK4OI4EIhyREqKY55/VmPY2DVmVCE1MTQgpCxDAQUQiRiLQoEccYiNhMxymvVstSqrBIlCYmc5vGyVQlBjcfhkFNmWUrijRN1827tkksUlXHfii1gCBBYpNEogPqqGoOEIlIDCERqKq6XoN7EArk6VpUAAAgAElEQVTMgcAAiEAAMYhAbJC/9N/ewY1v/50xNl1qu9g2sY0xMAd6+fVT3Lj/1jFuEMAA4Ybjxz766KN3v/P22995+53vvH364IRFIhOLMKBFHSCgadvdnZ3FfD7rWhZM02i1AlpK7vtxGMYpDznnWlRY2q5NTRIKLHiGATADBAITCAwmEAkTixALMwRgMEAEEgOZuaqZuqpWLaXUPE1Tvqa1mhuIgwizbBETABKOEpq26WbdfDZruy6E6G651GkYxilXraWUnHMppWp54cUXv/KVr/zv3/tP8cyDN++oOQARJiLHT0KOP2+52jw8Obt//8GP3nv/6fm5qQJg4ciypaWs+03NhZliSvPZPDZJhFVtmkaDx62U2q4hEXcnAocAQE03/XB1cbG6XK6Xq1JyEtnZ2bl9+3bbdqpV7Rozp5gcPk0TE7VdF0JwdwBE5Ftq6lZLBRyASEhNalJq2y7FJCGY+TDkkouWWkqpOQMehKNIE4NpHcdhnMaSp6urq7NHDx+dPT4/f1KrdvP5weHh3Rdf2N3b5RiOjo8/+/nPvfrqq4e3b33qG5e4ce+bR0RUce3jr5/ixodvHDOgJZf1elgul5eXOo0sLARzdRgJRDgkIUI1zTmvN+tpHLQqE5qYmhBSECKGgYhCiESkRYk4xkDEZjpOebVallKFRaI0MZnbNE6mKjG4+TAMasosW1Gkabpu3rVNYpGqOvZDqQUECRKbJBIdUEdVc4BIRGIIiUBV1fUa3INQYA4EBkAEAohBBGKDVKCSQBLFFJsutV1sm9jGGJgDOfDK66d45v5bxwQwQLjh+LGPPvro3e+8/fZ33n7nO2+fPjhhkcjEIgxoUQcIaNp2d2dnMZ/PupYF0zRarYCWkvt+HIZxykPOuRYVlrZrU5OEAgueYQDMAIHABAKDCUTCxCLEwgwBGAwQgcRAZq5qpq6qVUspNU/TlK9preYG4iDCLFvEBICEo4SmbbpZN5/N2q4LIbpbLnUahnHKVWspJedcSqlaXnjxxa985Ss/++UvfeFnvnjn+PjuV0/xzINvHhGR4ychx5+3XG0enpzdv//gR++9//T83FQBsHBk2dJS1v2m5sJMMaX5bB6bJMKqNk2jweNWSm3XkIi7E4FDAKCmm364urhYXS7Xy1UpOYns7Ozcvn27bTvVqnaNmVNMDp+miYnargshuDsAIvItNXWrpQIOQCSkJjUptW2XYpIQzHwYcslFSy2l1JwBD8JRpInBtI7jME5jydPV1dXZo4ePzh6fnz+pVbv5/ODw8O6LL+zu7XIMR8fHn/38537l734CN949eu3w1z8UoWuDGwMEkAOKl79+ihvv/ZNuXC6XFxdlmpgBV7iCQIFEmJgAL1amadr06zJlNxWiJrVNDE2MRARzgBhcqm76wdWaJsWYYoxq2m+GPGWDCkuMCUDV7A4WMbNxHGpVIhaRGELbtLN5F2MiJq217wczDUEkCocAZjjMUdQAlq2QUkwgMVWtWmuBe2ASZiEIbTEYzAIiMBvki3/7Odz49t8ZQ9PFto1tE5vYROFAL79+ihv33zrGDQIYINxw/Njpycn33v3dt9/+7X/1W//y/Q/ueVU3Z6EYYgqhaVLbzXbni52dnfm8izGqln6zznmCqxadcp7yuFVKdgUxQhCWKCwADOamriCAAhORsAAwOANMQsIhiAQJLCDBNQbIgKLuamrqalWv5VJyzqVkM3UHETMLC2GLmYWYJYik1LRbXdvNuiARQK2lH4acy1bNZcpjKerwl155+Rd+4S//vbf/Bp558MYdhQEkwkTk+EnI8ectV5uTk7P79x+8/977V5eXLBJDEAkhcCCqpfZDX0sBEd8QERYxtWmajL1rUtM2TduycFUFwCGAYOb9MCwvLy8vLi7PL8s4ppQO9vbuHB/N5wtXLVq33F1Y1HQaR4AWO4uUEm4QEQCrWmqZci65mCkzx5Sappl1s27WNakTllLMTEupZcx9vwZ81rVNSikI3DWXfujXq+XjJ4/vf/jh2dnj1fLK3GaL+cGtW3fv3t2/ddjOujvHx3/h05956eWXDm4dfOobF7jxwZtHzFQBBl59/RQ3PnzjmAErufT9uFwuLy7KNDEDrnAFgQKJMDEBXqxM07Tp12XKbipETWqbGJoYiQjmADG4VN30g6s1TYoxxRjVtN8MecoGFZYYE4Cq2R0sYmbjONSqRCwiMYS2aWfzLsZETFpr3w9mGoJIFA4BzHCYo6gBLFshpZhAYqpatdYC98AkzEIQ2mIwmAVEYDZIASqJk1BIoeli28a2iU1sonAgB155/RTP3H/rmAAGCDccP3Z6cvK9d3/37bd/+1/91r98/4N7XtXNWSiGmEJomtR2s935YmdnZz7vYoyqpd+sc57gqkWnnKc8bpWSXUGMEIQlCgsAg7mpKwigwEQkLAAMzgCTkHAIIkECC0hwjQEyoKi7mpq6WtVruZSccynZTN1BxMzCQthiZiFmCSIpNe1W13azLkgEUGvphyHnslVzmfJYijr8pVde/oVf+MuvfenLn/38X7x95/m7Xz3FMw++eUREjp+EHH/ecrU5OTm7f//B+++9f3V5ySIxBJEQAgeiWmo/9LUUEPENEWERU5umydi7JjVt07QtC1dVABwCCGbeD8Py8vLy4uLy/LKMY0rpYG/vzvHRfL5w1aJ1y92FRU2ncQRosbNIKeEGEQGwqqWWKeeSi5kyc0ypaZpZN+tmXZM6YSnFzLSUWsbc92vAZ13bpJSCwF1z6Yd+vVo+fvL4/ocfnp09Xi2vzG22mB/cunX37t39W4ftrLtzfPwXPv2Zv/ZrL+HGu0evHfz9D0WImWhyA0AAOaD+8tcf4sYH/6Trl8vlxdNpHBiAq1omphijCCncrOQyTWMexqGWDCCIdCk1KaUYmBlOWnQcptVqfXl5WWtt226xWOzu7cYQhinnPE1TdqCJiZjAIOYgwczHcai1gDiIxBiapp13XQwJsFLrMAxmaFKgwO4wwBwwqBuIY0wxptQ0zNFMtarWAncmCBETiFiYSIRZWMSYDfTFv3ULN779d8aQmtC2qW2alGIjHPjl109x4/5bx7hBAAOEG44fe/To0e99//vvfued3/qtf/HeH/9omrKqMlOT4my22N3ZOTzYP9g72N/fa9uWhco0Xl5crvt1zWMttVad8jgMo2oVCTBXU3cjFriXWtwcbiCJUViEwAAcBoDBQSSkKEGEBIAC5nBDVS1FTc1MXaEwdzVDVTWtZsZbRBzEAa0K5hBDDCIcJIYmxabtZrMuhMSCWnUa8zTlUvM05WHTF60Afezjr/6VX/zFX/vWX8czD954Xt1BEBEQAW4Ag/DvQo4/5bhG2GyGR4/OHzw4vffB/eVqlWIiJq+q7m7VVE2NmVKMajaOY9HKzKY25QlMXds2XdN2LbOUWgBIiMwE5mkal1fLi6dPz88e1zG3KR3s79994cXFYmGqWcs4jKoVQK2173ti3t3d7ZoWTD/m7qo6TdM4DMM4llLgHmNs2nYxX2x13SylNoQERyl52AxXy0uC7+/udl0XQxBidx36zdnjxycPPvrRe+89enQ2bPoQZf/W4Z07d45fuHt4eJi69tbt269+7GPHL9y9/fytT33jEjfuffOYCIZrr75+ihsfvnHEIKtFN5t+uVxePJ3GgQG4qmViijGKkMLNSi7TNOZhHGrJAIJIl1KTUoqBmeGkRcdhWq3Wl5eXtda27RaLxe7ebgxhmHLO0zRlB5qYiAkMYg4SzHwch1oLiINIjKFp2nnXxZAAK7UOw2CGJgUK7A4DzAGDuoE4xhRjSk3DHM1Uq2otcGeCEDGBiIWJRJiFRYzZQNVZASUhSSE1oW1T2zQpxUY4MICXXz/FM/ffOiaAAcINx489evTo977//Xe/885v/da/eO+PfzRNWVWZqUlxNlvs7uwcHuwf7B3s7++1bctCZRovLy7X/brmsZZaq055HIZRtYoEmKupuxEL3Estbg43kMQoLEJgAA4DwOAgElKUIEICQAFzuKGqlqKmZqauUJi7mqGqmlYz4y0iDuKAVgVziCEGEQ4SQ5Ni03azWRdCYkGtOo15mnKpeZrysOmLVoA+9vFX/8ov/uJf+tmf/anPfvr27efufu0hnnnwzSMQAW4Ag/DvQo4/5bhG2GyGR4/OHzw4vffB/eVqlWIiJq+q7m7VVE2NmVKMajaOY9HKzKY25QlMXds2XdN2LbOUWgBIiMwE5mkal1fLi6dPz88e1zG3KR3s79994cXFYmGqWcs4jKoVQK2173ti3t3d7ZoWTD/m7qo6TdM4DMM4llLgHmNs2nYxX2x13SylNoQERyl52AxXy0uC7+/udl0XQxBidx36zdnjxycPPvrRe+89enQ2bPoQZf/W4Z07d45fuHt4eJi69tbt269+7GN/7ddewo13j147+PWPghARKLsBIADupP7S1x/hxo/+cRqWy6urizL05AY4XJkhQZigpqXmKV8rJasWBgtRahIzC6FWHfpxs95cXi6Xl8uLy8taakjN3u7O7edv7+/ttbM5McYpuymJEMTZQpCUWncMw6bUSqAQYtem1DRdaplJtZpiy9zctOS87ocpjzkXNSfiEGJqmm42353vdLMuhAhHyVlNhcEsgYmYhYWYOEQWAZERf+FvHuDGd/5uz6mJqW3aJjYpNoEDv/z6Q9y4/80jEBHAuEYAHIDjxvmT8x/+4R999+13/vlv/ua//aMfTuNgbizctd3+7v7+wd6tg8O93cVsNoshAD5N03J51Q9DzqOWqqY152EcVZVFYD7lycwBuFrRqqpmxiwxxSDCTADMHAARsUiKkUWEGIACdg1atdSiVU3hrkaAwwE3d1MHmIVFoghgU1UQUpQgMaSUYowpNU1qUkMi7lpKncacS9aqecr9OGpVJ/rkJz71S7/0V/+7/++reObBG88rHCBiYiIjbDG2CNfcHFtMBIAccDiuEf7Eph/Pn1w8fHT24KPT5WotTOaupWit1RTwwBxDjDGZ6XqznnI2c3UzUxCFIE3TzGYzDmJVwYghcRAQpikvl8vL8/Ozh4+mfmxTOtzbf+HFl3YWO8W15HHTD6VkmE+1DEPPzLs7O03byla4xkBRHTb9xdXlsOlLKSDEmLq2Xczm3WzWdvOu62ZdxyJWtB+G5eWlwXcXi27WNSlFEQD9pn/06NHJ6YP79z988uTxMAwhhMNbz91+/vadO7f39g+6xfzWrVsvvPTSnePnbz138KlvXOLG/TePQXDC1iuvn+LGh2/cYUCnXPrNsFxeXV2UoSc3wOHKDAnCBDUtNU/5WilZtTBYiFKTmFkIterQj5v15vJyubxcXlxe1lJDavZ2d24/f3t/b6+dzYkxTtlNSYQgzhaCpNS6Yxg2pVYChRC7NqWm6VLLTKrVFFvm5qYl53U/THnMuag5EYcQU9N0s/nufKebdSFEOErOaioMZglMxCwsxMQhsgiIjLg6KdgASODUxNQ2bRObFJvAgQG8/PpDPPPhW8eMawTAAThunD85/+Ef/tF3337nn//mb/7bP/rhNA7mxsJd2+3v7u8f7N06ONzbXcxmsxgC4NM0LZdX/TDkPGqpalpzHsZRVVkE5lOezByAqxWtqmpmzBJTDCLMBMDMARARi6QYWUSIAShg16BVSy1a1RTuagQ4HHBzN3WAWVgkigA2VQUhRQkSQ0opxphS06QmNSTirqXUacy5ZK2ap9yPo1Z1ok9+4lO/9Et/9We//LOvfOJj+/v7d7/+EM98+OYdJjLCFmOLcM3NscVEAMgBh+Ma4U9s+vH8ycXDR2cPPjpdrtbCZO5aitZaTQEPzDHEGJOZrjfrKWczVzczBVEI0jTNbDbjIFYVjBgSBwFhmvJyubw8Pz97+Gjqxzalw739F158aWexU1xLHjf9UEqG+VTLMPTMvLuz07StbIVrDBTVYdNfXF0Om76UAkKMqWvbxWzezWZtN++6btZ1LGJF+2FYXl4afHex6GZdk1IUAdBv+kePHp2cPrh//8MnTx4PwxBCOLz13O3nb9+5c3tv/6BbzG/duvXCSy997X+8gxvvHr323K8/YAYIVMwAEIHd4f7C1x7hxh/+Ax9Wq83qKo8DuxF5EBICAepqteZSSs1aa1WFGbaYhVCrjuNw8fTi5OT04aOzJ2dPV6v1NIxF1RyLxfzo+OiVV1751E/91OHBoUJvWKk11ywh7O7uMfF6sy45A4gp7e7sNG0rJKplGqcQZH/v0E3Pnzx5/Pjx2aOz8/Pzy8urUkvTtLO26+bdwf7B0dHRc889t39wGESmcShVhSmEmFIKQRwgJogwC0RA8oX/ch83vvu/9CGmkFJsGmnSFgV++auPcOP+m3eIiLFFhC2HA3DcuLq8un/v3rtvv/3P3nrrD37/D0qpRGhSmM8Xh/uHu7u7O7uLFKOpqm2pqZaaq6qbm/6JPOWiaqbXaq2mbu5qVdVUi1YiSjHFKCQCwFXdsEVBUhCWIEwA1HCDTa3UoqWqmpuD4QQ4YFsKgEOMIaQYiVGquTsLxRib1LZdN591QYRYSinD0PfjMPW5aHGHmxVVU1Tzz3z607/8K9/4z//fr+CZkzfvOMjgbgYGETPhBgEwvwbQFoPggMHgABhEDBg2w/j06eWTJ08fP368XK7HadBSzNzcADBzlCBBQojmVnKeci45m3sIyWE5TxJksbPTpGTuQULTtSxSqw7j0K+W50+ePj457Td9FNnf2bt798XFzqKYTnnc9JuSi5kXLeM4EKGbzbuujSl1TdN2MwniqpeXVycnD1arNYFCCG1qUtu0sYlNihK7Wbe7uxtTgHsppV/31TRwaNu2m3UxBHP0ff/06dOLi4ury8vlcrVcLQHs7u4u9nb29/Z29/cODvZv3b599+7d28/f2tvf+cQvP8WNe28cASAhAC9/9RQ3HrzxPJnWaexX62G12qyu8jiwG5EHISEQoK5Way6l1Ky1VlWYYYtZCLXqOA4XTy9OTk4fPjp7cvZ0tVpPw1hUzbFYzI+Oj1555ZVP/dRPHR4cKvSGlVpzzRLC7u4eE68365IzgJjS7s5O07ZColqmcQpB9vcO3fT8yZPHjx+fPTo7Pz+/vLwqtTRNO2u7bt4d7B8cHR0999xz+weHQWQah1JVmEKIKaUQxAFiggizQAQkBjZAQSQSYgopxaaRJm1RYAAvf/URnnnwzSOACFsOB+C4cXV5df/evXfffvufvfXWH/z+H5RSidCkMJ8vDvcPd3d3d3YXKUZTVdtSUy01V1U3N/0TecpF1Uyv1VpN3dzVqqqpFq1ElGKKUUgEgKu6YYuCpCAsQZgAqOEGm1qpRUtVNTcHwwlwwLYUAIcYQ0gxEqNUc3cWijE2qW27bj7rggixlFKGoe/HYepz0eIONyuqpqjmn/n0p3/5V77xpS+/duv52/PF7O7XH+KZ+2/cBoOImXCDAJhfA2iLQXDAYHAADCIGDJthfPr08smTp48fP14u1+M0aClmbm4AmDlKkCAhRHMrOU85l5zNPYTksJwnCbLY2WlSMvcgoelaFqlVh3HoV8vzJ08fn5z2mz6K7O/s3b374mJnUUynPG76TcnFzIuWcRyI0M3mXdfGlLqmabuZBHHVy8urk5MHq9WaQCGENjWpbdrYxCZFid2s293djSnAvZTSr/tqGji0bdvNuhiCOfq+f/r06cXFxdXl5XK5Wq6WAHZ3dxd7O/t7e7v7ewcH+7du37579+5/+N/s4sa7R689/w8euDkAqmYAmEBwuN/96iPc+MHfz/1qNWxWOo0wFUIMzIBqsVpVteo1M8WWa9VrpdbNZnNxcfHw9NEHH7z/8PTJarkehrGUamYAxabZ3995+dVXP/+Fn37x5ZdmixmzTDmXLS0xpb3dfWHZjJucs5tHSTs7s5QSwFVLmaaY0q2DW3mc3n/vvXv37n10/8Ozhw+fnl8UrfOua2fdrOsODw/vbh0fHx0dLxYLEEBgkhAkpUQiTgARibAISwTTF//mLdz47t/bhBBiSqFJsWli01CQl752hhsfvnmHQSAQCFvugOOaA7RZrh49fPjd777zxm+88Qc/+EEplYiaNi1m893F7mw2a7oEYBqmogVwZtliggFwU3OtJedaaim1Wi2qpqpmquamZqqlZDCnmCSEIAygqrobACGRKFvMDIM7HGCCqZVSixZXhwEMYnLDlkMJIjGmFJqmEaaq7moOlyBN0866drGYp5QAySX36/W6X2/WwzRNVdUdABMJQJ//wuf/+q/+6n/yf30Bz5y8eQQih6ka4CTMRHjGfAtbRMwgOMzg7gCEiAB39Jv+7MnT8/OLq4ur1epqPQylZCYCsTCxSAyRmITYgFpLKXWaRgAhRTimnJnQdl2TEomkEJqmBVC0jNM09sPl+dOzk4fr1ZqBnZ2dF+6+OJvPS83DNK7Xm6KFQEVL3/dgLObzpm2bmLqumy0WMQa4P724uH/v3tXVMnIIMTZNk7ZCDCHEGNq23dndaZokLGqaS9GqcEiIXduFIGaYprxerTfDJuc89P3l5aWqNl3XzbrZrFvs7dw6PHzu9u3j46Nbh/s7e/OP/0fnuHH/jSMAJATgpa+e4sbJG7ddSxmG9WrVr1bDZqXTCFMhxMAMqBarVVWrXjNTbLlWvVZq3Ww2FxcXD08fffDB+w9Pn6yW62EYS6lmBlBsmv39nZdfffXzX/jpF19+abaYMcuUc9nSElPa290Xls24yTm7eZS0szNLKQFctZRpiindOriVx+n99967d+/eR/c/PHv48On5RdE677p21s267vDw8O7W8fHR0fFisQABBCYJQVJKJOIEEJEIi7BEMDnYQQaGSAghphSaFJsmNg0FceDlr53hmQffPCIQttwBxzUHaLNcPXr48LvffeeN33jjD37wg1IqETVtWszmu4vd2WzWdAnANExFC+DMssUEA+Cm5lpLzrXUUmq1WlRNVc1UzU3NVEvJYE4xSQhBGEBVdTcAQiJRtpgZBnc4wARTK6UWLa4OAxjE5IYthxJEYkwpNE0jTFXd1RwuQZqmnXXtYjFPKQGSS+7X63W/3qyHaZqqqjsAJhKAPv+Fz//1X/3Vn/3Z1xZ7O6lNd7/+EM/cf+M24CTMRHjGfAtbRMwgOMzg7gCEiAB39Jv+7MnT8/OLq4ur1epqPQylZCYCsTCxSAyRmITYgFpLKXWaRgAhRTimnJnQdl2TEomkEJqmBVC0jNM09sPl+dOzk4fr1ZqBnZ2dF+6+OJvPS83DNK7Xm6KFQEVL3/dgLObzpm2bmLqumy0WMQa4P724uH/v3tXVMnIIMTZNk7ZCDCHEGNq23dndaZokLGqaS9GqcEiIXduFIGaYprxerTfDJuc89P3l5aWqNl3XzbrZrFvs7dw6PHzu9u3j46P/4L9qcePdo9fu/IMHbg6AqioIDBA5HHe/+gg3fvC/jf16PWyWZZqgheFJyM1LzlqzmQEOgImIWVXHadj0/Wa9Ob94enJyevLgwYOT037dB05uGIYRQDPrmHgYx92D/U9+6pOvfvLjH//Yxxe7i36c3CymmNqmazsRKbWWWrRUAmJMwkIAGEQUJDYpXZw//f3f/70//uEff3Tv/pOzs/Vq7ebdfNbERMI7O/M7zz9/5+jo7vHdo+Oj528/N58vmJmYiBgEJwETyVbkIGD50t++gxvv/E8XElOIMTYptm3qWpb40tcf48aDN4+IABDc8accN4a+v3zy5Hd+53f/6W/8xg/+zfencXJYjDGl1KZWghDBVKdS4B5T6trUzWYSItxUvWoppZZSa5lK0VKLqVpVdXczd6+mWgrAIYYQhUkccFV3B0BEMUYWISYYzNUNW7pVajV1NQAkTCBmEBgAS0hNaprYtm2UaG5VtZTijhi4abudncV8tmjaBGAax+VqfXl5uVqt+k1fipHEFJv5fP6FL/7MN77xy7/yv34Kz5y8eQyCw83VASJiIjxjDncHaItB5HCDOojAuGbmq6vVycnD8/Ony+WqHzZTzmouN4IIiwgTiAGoas251KLm5sbEYIgIE8xMQuzaJoQImJmraa1q1dZXq7PTh8urpdU6XyxeevHFtpuP07DptzbqKiGUWpbrJYN293bbtgtRYtMsZvMYE4Crq8uPPvxwtVwJyVYMMaTYhBhTapum7Zq2bZsmhRCI2R3m5u4ABYkA3FBVS9FqCmAYhqvLiymXGGNKMTZxsZjvH+w9d+u5O0e3Dw4PFov5J375CW48ePMYgMFBeOmrD3HjwRvPWcl5GIbVql+vh82yTBO0MDwJuXnJWWs2M8ABMBExq+o4DZu+36w35xdPT05OTx48eHBy2q/7wMkNwzACaGYdEw/juHuw/8lPffLVT3784x/7+GJ30Y+Tm8UUU9t0bScipdZSi5ZKQIxJWAgAg4iCxCali/Onv//7v/fHP/zjj+7df3J2tl6t3bybz5qYSHhnZ37n+efvHB3dPb57dHz0/O3n5vMFMxMTEYPgJGAi2YocBCxOBDAIxCIxhRhjk2Lbpq5liWB66WtneObkm8dwx59y3Bj6/vLJk9/5nd/9p7/xGz/4N9+fxslhMcaUUptaCUIEU51KgXtMqWtTN5tJiHBT9aqllFpKrWUqRUstpmpV1d3N3L2aaikAhxhCFCZxwFXdHQARxRhZhJhgMFc3bOlWqdXU1QCQMIGYQWAALCE1qWli27ZRorlV1VKKO2Lgpu12dhbz2aJpE4BpHJer9eXl5Wq16jd9KUYSU2zm8/kXvvgz3/jGL//0z/x0N+9CjHe/fopnPnzztgNExER4xhzuDtAWg8jhBnUQgXHNzFdXq5OTh+fnT5fLVT9sppzVXG4EERYRJhADUNWac6lFzc2NicEQESaYmYTYtU0IETAzV9Na1aqtr1Znpw+XV0urdb5YvPTii203H6dh029t1B7/JvwAACAASURBVFVCKLUs10sG7e7ttm0XosSmWczmMSb8/3TB249l95Uf9u9av8ve+5xT975VdVc3m82bWhZljUiRlDQaz80BHIcEpYwxQJA8BQ6C5MEPyUuQIEEmSAIPjBgGAsGTv0ESyBnPwxjwIIichwnVF8oim7fuZldXdVXX/Vz2Pnv/fmutnDpUe2wD/nyA09OTx1tbo+HIkZsJPvgYCh9CjGVRlFVRlmVRRO89MZtBTc0MIO8CAFNkkZQkqwBomub05LjtUgghxhCKMBj0l1eWzq2du3jp/N/6b0rMfXDptfU/2YZhhrJkAEwgAgwbb+9h7u4/mTST8XQyTm2DLKTCpKqSuk5TVhUATGAmdi7nPG7qejIZTyYHh4dbW4/3dp+enpyaYWmwlFLef3pg4AsXLpB3J6dH2WywuHjt2tW/+RvfurS+bgA5djPeFzE45wASmHaiJgCYiAB2LoYItbabPn68/eGdu1sPH42Gp+PRqJk0IuKYvfdlGauyLHv91bXVK5fXNy9vbl67tryy5NgBECgABbNz7Bw573wgdt/7nzYx9//+bwc+xBB8KGIoy6LXcyFuvnuAuZ33LoEIM2b4a4a5adOcHh3duXX7Zz/96S8//GXd1KYSQnDOB+cJSJJVJEnnnev1+72qX/V6IQQRySI5p65LOZ3puiSSs4ipiogBpgZoysIE54L3jogAiIjhDAE+BO8cEZmZqJoYZkxTzipipgCYHBhETCDH7IIvY4xlWVVl8B6AZG27VlVAXMTY7/d6vX5VVczUpTyZjE9PTk9PhsPROKVEFKr+4Py5869957Uf/uhH/9H/voFndt5bNzLA1BQAE4EIz6jBzACaYRAZTKE4w4DCNOnJ8emjR1sHB4d13TTtVEVAHLx3IXjvmImJAahKyqltu5wzAFNVM3ZUliWzS6lzzlVVAXDXdQYE7wBI1slwfLC7PxwOU9v2ev0rm1fLqqrr8biu22mtqj6GtuuOTo9gtry8UvV6LrgixqqqnPOAjYejx9vb9WQSfCxCCDEWPvrgQwhFjCGGOeecd94RM75iMCM1mMEUYAYRM+eUToejlDoCOe+856pXLS4vrK2uXrx4fnl5qdevnv+DA8ztvL8OQEwBbL6zh7ntn61J27ZNXY9GzWQ8nYxT2yALqTCpqqSu05RVBQATmImdyzmPm7qeTMaTycHh4dbW473dp6cnp2ZYGiyllPefHhj4woUL5N3J6VE2GywuXrt29W/+xrcura8bQI7djPdFDM45gASmnagJACYigJ2LIUKt7aaPH29/eOfu1sNHo+HpeDRqJo2IOGbvfVnGqizLXn91bfXK5fXNy5ub164tryw5dgAECkDB7Bw7R847H4gdmEAMcuScDzEEH4oYyrLo9VyIYN585yme2Xl/HWb4a4a5adOcHh3duXX7Zz/96S8//GXd1KYSQnDOB+cJSJJVJEnnnev1+72qX/V6IQQRySI5p65LOZ3puiSSs4ipiogBpgZoysIE54L3jogAiIjhDAE+BO8cEZmZqJoYZkxTzipipgCYHBhETCDH7IIvY4xlWVVl8B6AZG27VlVAXMTY7/d6vX5VVczUpTyZjE9PTk9PhsPROKVEFKr+4Py5869957Uf/uhHX//G10MZnHcb7zzBM1vvnQfARCDCM2owM4BmGEQGUyjOMKAwTXpyfPro0dbBwWFdN007VREQB+9dCN47ZmJiAKqScmrbLucMwFTVjB2VZcnsUuqcc1VVANx1nQHBOwCSdTIcH+zuD4fD1La9Xv/K5tWyqup6PK7rdlqrqo+h7bqj0yOYLS+vVL2eC66Isaoq5zxg4+Ho8fZ2PZkEH4sQQoyFjz74EEIRY4hhzjnnnXfEjK8YzEgNZjAFmEHEzDml0+EopY5AzjvvuepVi8sLa6urFy+e//4/8Jj74NJrG3+yjTnKkgEwgQgwbLy9h7nb/3hUj0dtM+mmjaWM3MHEVCSLJsm5FRUzJSLvXBKpm7ptWxEZDoePd56Mh2MARVEsLqwMT05/+a9+pWIvvfzSYHFhOBodHh/t7j1dXVv97ve/9/LLLy2trLjgxnUtIt776EOMBXmGqqmJqpkR4J2vinI6bfafPv38s8/v3rkzOh2dW13xLtTj8XhSj0enIRTr65dijOPxuCjixuX1jcuXN69cWVpe8c6BIapqBnJgYhecc+wjnPud//UG5n7+v+z5EF30caYoi8HAF/Hqu4eY23l/HTNmmFEDE84Y5pq6Pj08unPr1k9+8tNffvjLppmISpjx3rEDkFMnIgYUZVxcXOxVfecdABHJWbqZlFLXpZmcc0qqYgZTMyiBDSpqROSdI2JiAmAqZpghgveBicAM1SwCwLMDkHI2UTUBQMQEMhiByHHwvihiUZa9suejd+xERbJkEVMhdjHGEHwRI8Bq0rXdZDIeT+pp07Rt7pIuLa88//yNN9544++8/Xd/739YxjPb711SGGBMOEOEf4MazAygGQaRwRSKMwyIaWrT4cHxw4ePDg4PmmkrWQCwc0URQ4jOkWMmIlVLuZuZTqeSMwA1lZTJu6qqQohEcI6ccypSNw0xLy4seefa6XR8Ojo6OBoNR20z7VW9K5ubRVWNx+NJU0+nDaAhxKZtdvf3VGR5Zbk3GBRzIQQm0izD4Wh390nqusFMf9Dr9auyYOIZx8TEYCJmAExg58mxYwdAFWYAiJgdezAzkYql3KUsMAUMTEURl5YXV1ZXL5xfW1peqMri2o+eYm7n/XUAYgpg8509zG39dE3aZjqpm/GoHo/aZtJNG0sZuYOJqUgWTZJzKypmSkTeuSRSN3XbtiIyHA4f7zwZD8cAiqJYXFgZnpz+8l/9SsVeevmlweLCcDQ6PD7a3Xu6urb63e9/7+WXX1paWXHBjetaRLz30YcYC/IMVVMTVTMjwDtfFeV02uw/ffr5Z5/fvXNndDo6t7riXajH4/GkHo9OQyjW1y/FGMfjcVHEjcvrG5cvb165srS84p0DQ1TVDOTAxC4459hHOMfsiAnsnA8+RBd9nCnKYjDwRSS4zXef4pmd9y5hRg1MOGOYa+r69PDozq1bP/nJT3/54S+bZiIqYcZ7xw5ATp2IGFCUcXFxsVf1nXcARCRn6WZSSl2XZnLOKamKGUzNoAQ2qKgRkXeOiIkJgKmYYYYI3gcmAjNUswgAzw5AytlE1QQAERPIYAQix8H7oohFWfbKno/esRMVyZJFTIXYxRhD8EWMAKtJ13aTyXg8qadN07a5S7q0vPL88zfeeOONv/P23335lZfYMzFtvPMEz2y/fwEzRPg3qMHMAJphEBlMoTjDgJimNh0eHD98+Ojg8KCZtpIFADtXFDGE6Bw5ZiJStZS7mel0KjkDUFNJmbyrqiqESATnyDmnInXTEPPiwpJ3rp1Ox6ejo4Oj0XDUNtNe1buyuVlU1Xg8njT1dNoAGkJs2mZ3f09FlleWe4NBMRdCYCLNMhyOdnefpK4bzPQHvV6/KgsmnnFMTAwmYgbABHaeHDt2AFRhBoCI2bEHMxOpWMpdygJTwMBUFHFpeXFldfXC+bU3/ivF3AeXXlv/p9tEmKEsGQATiADDxtt7mPvFPzppJuNpU6dpk9updZ1ZFhGIZMk5JZUMM2Ly3ptp3UyzZCIej8fbO9tN3RRVVcaqjMX+3sEHt27nLDe//vXz59fUbO9g/+OP7oWy+M4b3/na129eubZZleVwNE5dR+xC9FVRhhgxZyqigKp3XIQ4HA7v33/w6aeffvSrX0H15suvDPqD4+OTg/297Z3dXlXevHmz7FVPd3cBnL94/tKlixsbG0uLS857MJuqwQQEYmbHzsF59uE/+OOvYe7nf7TjQ+QQffCxqqrBIBTl1R8eYm7n/XXMmEENM0w4Y5hr6vrk4PDOrVvvvffe3Q9/OR4OVZP3wXsffIBp12UzBaEsy+XlxbKsAGRVSTmJdGfanHI6k1Ukq5gZ5ogIgJkRkWdHTEQwg6qoYcYRs3PERIABKkIEz4EAUVURg5kp5swwQwTnQ1EUVVkWZVnEyM4RwQyqYmJiimdMFbCUJaczXZe7NjXTvLZ27hvffPXN7771e3/797//35Z4Zvu9S6oGNqYZGP4tajAzgGYYRAAMppghRk65abqD/cMHDx8dHh2lLqsKs3PeFUURQvCOQAxARVLq2m7aNE1KGSKGMyH4sqx8CAY5k1MzbZum9s4tLa9UZQlFUzfHh8fj0aSdtlVZbWxshKIY13XT1Cl1gPkQmrbee7qXclpcWhosLPR6vaqsQoxMkCzDk9Od3Sep6xYXFpYWFxcHC0VVOSLMqGGGoQYRASF4zz4E7xhOTQFmcuwcOc/MqpgxqKppFoEAWpTl0tLC6urKufNri4N+KPzVd/cwt/3eOhHEFMDmO3uYe/TT1dQ03WRcj0bNZDxt6jRtcju1rjPLIgKRLDmnpJJhRkzeezOtm2mWTMTj8Xh7Z7upm6KqyliVsdjfO/jg1u2c5ebXv37+/Jqa7R3sf/zRvVAW33njO1/7+s0r1zarshyOxqnriF2IvirKECPmTEUUUPWOixCHw+H9+w8+/fTTj371K6jefPmVQX9wfHxysL+3vbPbq8qbN2+Wverp7i6A8xfPX7p0cWNjY2lxyXkPZlM1mIBAzOzYOTjPPjh27D0750PwIXKIPvhYVdVgEIqSyG2++xTP7Lx3CWqYYcIZw1xT1ycHh3du3XrvvffufvjL8XComrwP3vvgA0y7LpspCGVZLi8vlmUFIKtKykmkO9PmlNOZrCJZxcwwR0QAzIyIPDtiIoIZVEUNM46YnSMmAgxQESJ4DgSIqooYzEwxZ4YZIjgfiqKoyrIoyyJGdo4IZlAVExNTPGOqgKUsOZ3puty1qZnmtbVz3/jmq29+963f+9u///yN6yCAsfHOEzyz/f5FIhj+LWowM4BmGEQADKaYIUZOuWm6g/3DBw8fHR4dpS6rCrNz3hVFEULwjkAMQEVS6tpu2jRNShkihjMh+LKsfAgGOZNTM22bpvbOLS2vVGUJRVM3x4fH49GknbZVWW1sbISiGNd109QpdYD5EJq23nu6l3JaXFoaLCz0er2qrEKMTJAsw5PTnd0nqesWFxaWFhcXBwtFVTkizKhhhqEGEQEheM8+BO8YTk0BZnLsHDnPzKqYMaiqaRaBAFqU5dLSwurqyrnza9/6z6eY++DSaxd/vM0MIlBKiRhMM4Bh4+09zP3VHx83k/G0GXdNI9NGuk5yqzmLiomYKsyI4Z2LRQGgaadmFnw4HQ0fPHgwnkwG1QDgpm52d3Y/vncvZ7l+48bGxsba2upwMvn444+yyPUbN1544YUXX3llaWWp6zrJAsB5F2MMIXrviInBampZYEZEhweHH3/88Reff/blwy+rqnrtW78x6PcfP3785cMHn39xf3Fx8P3f/MHayuru3pOU0tLy0tra2sWLFxYWFhw5cg5MANRMMONABA4uuP/wH38Tc//yj574EMhH510sy97CQijLqz86wtzO++uYMYMaZphwxjDX1PXp4eHd23f/9M/+7MMP7w5PjruuY+eCc0WMIE4pqQqAoowL/YWijACLatd1KaeuTV3qUteJSBbRLAaboRkQmGbMDIB3TGCDqkFVoQamGc+OmACYmpgyIYTowKKqJlAYzEzNIKIGI5D3HGMsYlmUccb54L2bYSI1kyzNdJq6rm2nOYuqYI7AAKUkk6a7eHH9zTffePN73/3+D773xj8IeGb7Z5cECsA5EJHhjMIAMEgNZgbQDBOR4YzhK12XxqPmcP/o0aPHR6enKXUwOHY++iJG7x0RA2pmOUvOXTudaaZtm1Ji4n6vKsuqKgqF1c1kNBqenpyMJ5PUtUzcG/SXFpdWV9dgdHxwMpnUKeUiFmtrayHGumm63JkKAA6+7ab7Rwc5536/PxgMFgYLM71e5b3XLMcnJ48eParretDrDfr9xcFiWZWeHQGmogB7B0PbdQBCjD74IkbHTg1MzM555zkEA2kWUSMiM0upUxMQyqJYWV1ZXV0+d26136+co8vv7GJu62frzBAVYmy+8xRzj36ymupJMxnXo3EzGU+bcdc0Mm2k6yS3mrOomIipwowY3rlYFACadmpmwYfT0fDBgwfjyWRQDQBu6mZ3Z/fje/dylus3bmxsbKytrQ4nk48//iiLXL9x44UXXnjxlVeWVpa6rpMsAJx3McYQoveOmBisppYFZkR0eHD48ccff/H5Z18+/LKqqte+9RuDfv/x48dfPnzw+Rf3FxcH3//NH6ytrO7uPUkpLS0vra2tXbx4YWFhwZEj58AEQM0EMw5E4OCCcz764EOIPkQfAvnovItl2VtYCGVJ7DfffYpndt67BDXMMOGMYa6p69PDw7u37/7pn/3Zhx/eHZ4cd13HzgXnihhBnFJSFQBFGRf6C0UZARbVrutSTl2butSlrhORLKJZDDZDMyAwzZgZAO+YwAZVg6pCDUwznh0xATA1MWVCCNGBRVVNoDCYmZpBRA1GIO85xljEsijjjPPBezfDRGomWZrpNHVd205zFlXBHIEBSkkmTXfx4vqbb77x5ve++/0ffG/z2lXMMDbeeYJntt+/QESGMwoDwCA1mBlAM0xEhjOGr3RdGo+aw/2jR48eH52eptTB4Nj56IsYvXdEDKiZ5Sw5d+10ppm2bUqJifu9qiyrqigUVjeT0Wh4enIynkxS1zJxb9BfWlxaXV2D0fHByWRSp5SLWKytrYUY66bpcmcqADj4tpvuHx3knPv9/mAwWBgszPR6lfdesxyfnDx69Kiu60GvN+j3FweLZVV6dgSYigLsHQxt1wEIMfrgixgdOzUwMTvnnecQDKRZRI2IzCylTk1AKItiZXVldXX53LnVr/2nQ8x9cOm1Cz/eJoAZlFIiBtMMYNh4ew9zf/UPD5vJuK7HXV13TaPdNKdWJYsomzGBmL13RQxFUYC561oDiqI4Oj7+5JNPj4+Pi1jmLh0fnjx5srf1aEuAjY2Nzc3LVzavppw/+vijSdOcP3/+uevX/8Y3v3Hx0joIZ0zBHLz3Lnjv2DvPzlRzypplZnd39+6Hdx/cv/909+nS4uC13/h2WRb3v/j8s8/vf/H5FwuLCz/4rd9cv3jx5HQoIr1+uba2tnnlyuLighkAJiIDsllSMYWAwOydf/v/fA1z//KPdnyI5Dw5jmXVW1goqt7V//gIczvvr2PGDGqYYcIZw4zaZDx+urt7986df/4Xf/GrX310enw8nTbExM4VMTpwFjEoMWKIVa8XQiSGinYpd13btV0+k0Qki5qqmakaM51hJiITBdNXANiMqMEAEIgcExEAmyOi6D0Rm6mpATBTM4iowWAGgJ0LwccQiyLGUIQiFjGG4Nk5mLVdV9dNU880KXVqBsAxETliFtFmKleuXPnBD37rre9999tvvPb6f014Zvu9S6oGNqYZGKAwzDFIDQYDiEBMRIa/Zpg23eh0vL9/9Hhn53Q4ylkAeO9C8CEEx45IzUxURXLquradNpN62jTNtGGifq9XxsIFN22a/cP9o6PDk5PTadOICgBmLC0tb25e7VeDpm5TEgaFUPT6PXYu5aSqYGJH5FyX0rgeq0gRY1FV/V6v3+/1+4PgnJqenJ5ubW01dd0vq16vPxj0q1gQOYaKCohD8EpIKTMhhCLEWMTIzqkYs/PehRBdERkuJxFTAKqSUqcqICuqanVleXV1ee3cSq8qibDx9hPMPfrZOgEGIcbmO08x9+gnK21dt5NxPRw2k3Fdj7u67ppGu2lOrUoWUTZjAjF774oYiqIAc9e1BhRFcXR8/Mknnx4fHxexzF06Pjx58mRv69GWABsbG5ubl69sXk05f/TxR5OmOX/+/HPXr/+Nb37j4qV1EM6Ygjl4713w3rF3np2p5pQ1y8zu7u7dD+8+uH//6e7TpcXBa7/x7bIs7n/x+Wef3//i8y8WFhd+8Fu/uX7x4snpUER6/XJtbW3zypXFxQUzAExEBmSzpGIKAYHZOx9iGWIMRfQh+hDJeXIcy6q3sFBUPXJ+892neGbnvUtQwwwTzhhm1Cbj8dPd3bt37vzzv/iLX/3qo9Pj4+m0ISZ2rojRgbOIQYkRQ6x6vRAiMVS0S7nr2q7t8pkkIlnUVM1M1ZjpDDMRmSiYvgLAZkQNBoBA5JiIANgcEUXvidhMTQ2AmZpBRA0GMwDsXAg+hlgUMYYiFLGIMQTPzsGs7bq6bpp6pkmpUzMAjonIEbOINlO5cuXKD37wW29977vffuO1y5c3AAOw8e4untl+/yIRDFAY5hikBoMBRCAmIsNfM0ybbnQ63t8/eryzczoc5SwAvHch+BCCY0ekZiaqIjl1XdtOm0k9bZpm2jBRv9crY+GCmzbN/uH+0dHhycnptGlEBQAzlpaWNzev9qtBU7cpCYNCKHr9HjuXclJVMLEjcq5LaVyPVaSIsaiqfq/X7/f6/UFwTk1PTk+3traauu6XVa/XHwz6VSyIHENFBcQheCWklJkQQhFiLGJk51SM2XnvQoiuiAyXk4gpAFVJqVMVkBVVtbqyvLq6vHZu5cU/PMbcB5deu/DjbQAEUJYMgAlEgGHj7T3M/dU/PKzHo3oynk7GqW5S12juRDIMnimGM0UMRRHLsiJGmwVAEeP+wf6du3d3n+ypaDOpDw9Pjg+Ojk5OGVg7f25jfePa89dF5N69T4aT4eLi4rXr119//fXLVzeLovDeETG+QuSdY8eenBlS6lLXdW23/Xjr9t27Dx88ODk8Xljo3/zaTefo808/e/Dg4eNHO1W/evXVr69vXAre+xCKGC+uX7z5tZurqyvTrhMRgEQtZUk5pWQCNTAF/8Mfv4G5f/lHO857dgFMoSwHi0ux6l37g2PM7by/jhkzqGGGCQSYQQ2wo8OjB1988eGdOz//+c8/++zz0elxM22yKBMVofDeYYbAjoP3sShC8MxO1VJKXde1XZdSUhFVETUTVVPMMTE5BmCimGGaAWAzqmaGOSYGE76iBhAxEc6YGp4xGMxMjZiIOIbgg/MhljH2+r0QYwyRmGCWutxM67qZTqdNSllFzNQMInZGSUDXrz//O7/7u2+99dY3vvXqt/5LxTM7760bGQAiM0DNDAaAQCAAZIYZImIQGX7NAEXTTI+OTg/2D3f39oejsYqAyfsQggveM7OqzKippNx1Xds2TT2pJ5PxeCw5B+8AyzkfHx09ePDF0fFRzgKAGSml8aTu93rXrz1//vyFquqXVa8oquADMwMQmGP2MTjvwQSCiAJGjs8QhRiKonTOwbSejHf39lLXDfqDXlnNBOegpqYwZcehKNg5mDoXirLwoQzeAZSyELiIMRaxrHrsA3TGRCXnlFLOkgyoqnJ5eXl1dWltdanqlQA23n6Cua2frYMBKIDNd/Yw9+gnq6meNJNxPRzW41E9GU8n41Q3qWs0dyIZBs8Uw5kihqKIZVkRo80CoIhx/2D/zt27u0/2VLSZ1IeHJ8cHR0cnpwysnT+3sb5x7fnrInLv3ifDyXBxcfHa9euvv/765aubRVF474gYXyHyzrFjT84MKXWp67q22368dfvu3YcPHpwcHi8s9G9+7aZz9Pmnnz148PDxo52qX7366tfXNy4F730IRYwX1y/e/NrN1dWVadeJCECilrKknFIygRqYgi9iGYsiFGWI0XnPLoAplOVgcSlWPfZh892neGbnvUtQwwwTCDCDGmBHh0cPvvjiwzt3fv7zn3/22eej0+Nm2mRRJipC4b3DDIEdB+9jUYTgmZ2qpZS6rmu7LqWkIqoiaiaqpphjYnIMwEQxwzQDwGZUzQxzTAwmfEUNIGIinDE1PGMwmJkaMRFxDMEH50MsY+z1eyHGGCIxwSx1uZnWdTOdTpuUsoqYqRlE7IySgK5ff/53fvd333rrrW9869WLFy8YDMDlH+7hmZ0/vWiAmhkMAIFAAMgMM0TEIDL8mgGKppkeHZ0e7B/u7u0PR2MVAZP3IQQXvGdmVZlRU0m567q2bZp6Uk8m4/FYcg7eAZZzPj46evDgi6Pjo5wFADNSSuNJ3e/1rl97/vz5C1XVL6teUVTBB2YGIDDH7GNw3oMJBBEFjByfIQoxFEXpnINpPRnv7u2lrhv0B72ymgnOQU1NYcqOQ1GwczB1LhRl4UMZvAMoZSFwEWMsYln12AfojIlKzimlnCUZUFXl8vLy6urS2urSjT88xtwHl1678ONtAsCgLJkJM0SAYePtPcz91R8fj4enk/HpdDLu6ka6qeQOpo7Ye1fGoizCTCxiWRTEnFWYXVHEg4OD23fvPn68Pa2b8WhycnQyHE7qycQ5t7yycv78+QsXL07b7tNPPxmNRovLyzdeeP6NN9+6dv16VRUhRGIGYCog8s45cuxYDZJTN23revLlo0e3b926f//BydFRVVUvvfRC8G57a+vJzu7e3qF3fOXqxoUL5xYWFgaDQb/fv7J55RuvvrqyulrXdeo6NYhIm1KXcpckqwjIufDDf/om5n7+P295H8k5IxfLqr+0WPZ61/7gBHM776/jK2aYIQIMZlA11Z2dnQ9v375z+87tW7cfPd6aTMbTpkldAlAW0TnH5HxwMYYQvA/BOQewmqa261Lqui6lbKaqImomqqaqBoCZ2DkApgqAQGDCjJrBZlQNADOdAYEJM2owMzXMEROeMTXMsXPes/MheF8UsT9YqIrSB08EVU0pN9Mz7bRNKamJqWURFc2iIPYu3rjx0u///u995403vvbqzW/+FxnP7Ly3DgLIZhRmMDxDRADBABAIDCLDrxmgmIyb/YOjg/2D3af7k3EtgGPyPoTggveAiYjKmSwpdW07ndaTuqnHM+10qiqpbcf1+PDwcHtrazwZh+gJrCJJkiQpiri8vLKyvLq0fG55eXVleSWUpeRsADvngy+KwnmvADti5wGoiJgCmTfUBgAAIABJREFUSkQhBO8cE7Vde3x8rCKL/UERC+ccASZKDO/Yex+K6LwHsXOuLCofPBGboBNhorIoi7Kq+r0QghnMVERyzimlLNmAWMSlpYWV5aWV1aVeVRJh4+0nmNt6b50JasqEy2/vYe7RT1bTtG7H4/FwNB6eTsan08m4qxvpppI7mDpi710Zi7IIM7GIZVEQc1ZhdkURDw4Obt+9+/jx9rRuxqPJydHJcDipJxPn3PLKyvnz5y9cvDhtu08//WQ0Gi0uL9944fk33nzr2vXrVVWEEIkZgKmAyDvnyLFjNUhO3bSt68mXjx7dvnXr/v0HJ0dHVVW99NILwbvtra0nO7t7e4fe8ZWrGxcunFtYWBgMBv1+/8rmlW+8+urK6mpd16nr1CAibUpdyl2SrCIg54KPMRZFLMsYo/eRnDNysaz6S4tlr8c+bL67j2d23l+HGWaIAIMZVE11Z2fnw9u379y+c/vW7UePtyaT8bRpUpcAlEV0zjE5H1yMIQTvQ3DOAaymqe26lLquSymbqaqImomqqaoBYCZ2DoCpAiAQmDCjZrAZVQPATGdAYMKMGsxMDXPEhGdMDXPsnPfsfAjeF0XsDxaqovTBE0FVU8rN9Ew7bVNKamJqWURFsyiIvYs3brz0+7//e995442vvXrz3PlzagZg80d7eGb7/YsKMxieISKAYAAIBAaR4dcMUEzGzf7B0cH+we7T/cm4FsAxeR9CcMF7wERE5UyWlLq2nU7rSd3U45l2OlWV1Lbjenx4eLi9tTWejEP0BFaRJEmSFEVcXl5ZWV5dWj63vLy6srwSylJyNoCd88EXReG8V4AdsfMAVERMASWiEIJ3jonarj0+PlaRxf6giIVzjgATJYZ37L0PRXTeg9g5VxaVD56ITdCJMFFZlEVZVf1eCMEMZioiOeeUUpZsQCzi0tLCyvLSyurSi394jLkPLr128cfbYDCBVAUAkWHGsPH2Hub+v390Mjk9GZ4eN+NRN6lzaqHZEfkQyhCKGHwIwTvnXQjROTLAhdDv9Y5PTu7du7f9eHt4OhyO6mY8GY/rZty44FZXV3v9QQz+8Ojoo3ufNM300uX1V155+c233rx27bmyKrwPjh0ANXXMznnnHRPDVNXadjoejh48fPiLDz745NPP9nZ3ihBffunG4uJS00zGo/Hx8UmS1KvKfr+3MFhcXV29dOni1eeee/nmzYWlpelo3HZdljNtmsltSklEFGD3oz/5Lub+n//xSxciu6CEUFULS8tVv3/tD04wt/P+Ov4dZlA1FUn5/v0v/vIv//L2L2599vnnh/sHXZp2024GsBBi8N55V8Sy1y+KovQ+gJGTpJy6NnWpS10nIqZmUDXojIiomhkAJmYmVQPgHBMIc2qqagbDnGOmGWYigpkmMRj+PQhEjme8c8RUltXi4mJVlcwOgKqkLrXTtu3arutSziaqJmbIoirKzsdQvfjSS7/z27/77Tdff/nmK9/8+wnP7Ly3DoLB1NRggGGGCAARMUhBABgEgAy/ZjDBaDTZe7q/93R/f29/UteAY+didH7GOTVTSVmyieScUtd17bSp66au62nT1vW0aUbj4enJyXg87tqGiXr9fkr56PgoOF7f2HDkDg4PklivWlo7d/7Klc3ewoKIEJEPIcRQFAU7TiIECjEYrJlORbJj4hliF3wRg6pO6xqGXq8XmFNOIgKz4MOg3w9FBBE7ds55NxOJSdRMVbOxd1VVlWWv7PW9d2aYIYKqtl2XJZuKD77f7y8vLyyvLPd6lWO6/M4TzG2/v04EMwNw+e1dzG39dE3a6XQyHp2eTk5PhqfHzXjUTeqcWmh2RD6EMoQiBh9C8M55F0J0jgxwIfR7veOTk3v37m0/3h6eDoejuhlPxuO6GTcuuNXV1V5/EIM/PDr66N4nTTO9dHn9lVdefvOtN69de66sCu+DYwdATR2zc955x8QwVbW2nY6HowcPH/7igw8++fSzvd2dIsSXX7qxuLjUNJPxaHx8fJIk9aqy3+8tDBZXV1cvXbp49bnnXr55c2FpaToat12X5UybZnKbUhIRBdj5EGIsQlkWsXAhsgtKCFW1sLRc9fvkw9V39/HMzvvr+NfMoGoqkvL9+1/85V/+5e1f3Prs888P9w+6NO2m3QxgIcTgvfOuiGWvXxRF6X0AIydJOXVt6lKXuk5ETM2gatAZEVE1MwBMzEyqBsA5JhDm1FTVDIY5x0wzzEQEM01iMPx7EIgcz3jniKksq8XFxaoqmR0AVUldaqdt27Vd16WcTVRNzJBFVZSdj6F68aWXfue3f/fbb77+8s1Xzp1bUzMAmz/awzNb710wGGCYIQJARAxSEAAGASDDrxlMMBpN9p7u7z3d39/bn9Q14Ni5GJ2fcU7NVFKWbCI5p9R1XTtt6rqp63ratHU9bZrReHh6cjIej7u2YaJev59SPjo+Co7XNzYcuYPDgyTWq5bWzp2/cmWzt7AgIkTkQwgxFEXBjpMIgUIMBmumU5HsmHiG2AVfxKCq07qGodfrBeaUk4jALPgw6PdDEUHEjp1z3s1EYhI1U9Vs7F1VVWXZK3t9750ZZoigqm3XZcmm4oPv9/vLywvLK8uv/CenmPvg0mvrf7INgAikKgCIDDOGjbf3MHfr/xiOTo5Pjw/Ho2FX15Y6mATHMRZFjNHPMLHzjrwP5AjEIcZeVdX15MH9BztPdg+Pjsenp/W4aZpp13bOhcHCgNhNm+bg6PDx1mOBXrl85cWXXvjGq9+8eu3qwsJiiBGmMDOA2cUiOueYiHBm2rbDk9Mvv3x4587dTz79ZHtrC8C1zSurq8uxiCIyHo27rgU0xjjoDc5fuPDCjeevPvfc+pWrVb9qJ03quqySRbJIStLm1ImJiIJ++E+/i7n/+79/6GMk74xdLKuF5eWy13/u751gbuf9dfw7zCCSJaeu+/TevT//Z39+6xe3drZ3TobHJtKl1E6npubngndlWfb6vbIsvQ8G7ZKk7tdyTiqGOYNmURXJIqYKgEBgAkAzIOeYQAAMJqJqCjUwzXjniNk7hiFnMVGDAVAzAKaGOWJiImYGEc8QlUW5uLxUlqUjBiCmKaVuOp12Xdd2OXdmEFEABsDAPhRF9cKNF//Wb//2t19//eWvv/LNv5/wzM7P1uFgUBEBDEwAgTBDRAxSEAAGASDDV0ygauPhZHt3d29v/2B/fzxpQOScK3xg75hJ1XLuTERVRJKm1HZtM62nTdPWTTNt2qYZDU8PDw+zdAuDQa+snHfD4enDL7/s9/tvfuf1GIuPP763+/RAlZdWzr/44ksra2tMxM654EMMIUYDpZQAhBhUZFxPcs7eETsXQihi7FWliU7G49QmdpS7blyPJWdH3Ov1VtdWB4MFF3yMMcQYQnDsxSxnmQEQXKx6VVn1iqJk72EGImY2Q0qdiCTpnHNVVSwuLKysLPd6lXO8+e4u5nb+dB2AmQG4/PYu5rZ/dk7atm0mo5OT0cnx6fHheDTs6tpSB5PgOMaiiDH6GSZ23pH3gRyBOMTYq6q6njy4/2Dnye7h0fH49LQeN00z7drOuTBYGBC7adMcHB0+3nos0CuXr7z40gvfePWbV69dXVhYDDHCFGYGMLtYROccExHOTNt2eHL65ZcP79y5+8mnn2xvbQG4tnlldXU5FlFExqNx17WAxhgHvcH5CxdeuPH81eeeW79ytepX7aRJXZdVskgWSUnanDoxEVGQc8EHH4syxsrHSN4Zu1hWC8vLZa/PIVx9dx/P7Ly/jn/NDCJZcuq6T+/d+/N/9ue3fnFrZ3vnZHhsIl1K7XRqan4ueFeWZa/fK8vS+2DQLknqfi3npGKYM2gWVZEsYqoACAQmADQDco4JBMBgIqqmUAPTjHeOmL1jGHIWEzUYADUDYGqYIyYmYmYQ8QxRWZSLy0tlWTpiAGKaUuqm02nXdW2Xc2cGEQVgAAzsQ1FUL9x48W/99m9/+/XXX/76K+fPrZkZgMs/2sMzj352DjAwAQTCDBExSEEAGASADF8xgaqNh5Pt3d29vf2D/f3xpAGRc67wgb1jJlXLuTMRVRFJmlLbtc20njZNWzfNtGmbZjQ8PTw8zNItDAa9snLeDYenD7/8st/vv/md12MsPv743u7TA1VeWjn/4osvraytMRE754IPMYQYDZRSAhBiUJFxPck5e0fsXAihiLFXlSY6GY9Tm9hR7rpxPZacHXGv11tdWx0MFlzwMcYQYwjBsReznGUGQHCx6lVl1SuKkr2HGYiY2QwpdSKSpHPOVVWxuLCwsrJ88z8bYe6DS69t/F/bmCMzBQwwzBg23t7D3N1/Mjk5Ojo53B+dnqZpbaljRnAcQ4wu+OCYiB07R945cgwiH0JZFNNpu7e393R/f//pwfHR0fBk2LYdg0HEzM10enpyMmkaVS3K8uLFC5fWNzY3r2xc3rh0cb3qVanrchYAPriyrHzwTExMjnk67U5Pjh4/fnzvk08e3H+wtbU1rScLC/2lxcXVtdUQfNs0kjMYwcWqV22sr9+8+bX1jY2i6jNTnhExgwFmENUkkrJ02UT17R+/gbl/8d99EWJkH8m7oqr6S8tlr/fc3zvF3M776zDDDBG+ImoqOXUzH3300Z++/6d3b9/Ze7o3GU1UJeVuOp1qFue9JybHRYi9flWU0fkAQLJ0KXVdl1In8v8TBW8xll5Xftj/a6299/d955y6dXd1V1eLTYoakeKQI15FW6NRZmB4BolnRIEae/wQ2AgCOwjyMAlgIMl7rggQIAGSKI7zlDxmYlrkzATB+EG2AUOGJVGUSEqieO+u6rqfU1Xn9u2911o5XXHbv5+aQ4hxpZpqrVXVqjqcQACIiUAkLMxMDMDhqmZubgaAmIOIhBBDIEBXqjnc1Ryuag7HFQIRkxATE640qVnbWG/bVkSw4laq5n7Z93m5XJRSqyoAJmIWFgmSYmyfePJL3/zmN19+9ZXnfuPZ5/9DxSP7b+xASF21VsA5BCJydxAIxEQGwhUG4ER4yNVV/WJyuXdwcHR4fHR8PJ8tQMQrIsIEQFVrza4KN3PzqkVL7peL+Ww+nebcA5jPpqfHxzHK43fvdl07nV7eu3f/7Z/+9Mb1G3/011+/trn145+886sPP35wcNYNRs999YU7d+40qYkpgpiFJQY11FoMxsJa6mK5KCUDHlMaDYbD0XAw6ErfHz44PD07nV5cXpyfjcdnmkvbtFvXrt3e3bm+fXNjc3N9fX00GjVNx0HMkPulmjMoptR1w6btYkrE4m5ELEQAqtaqWjQzoWnSYDjc3NwYDQaxCXdfP8SV/bdu41/x3W8d4Mr+G9uuZTmfTSeTydnZ5PT48vy8LOdeMjOicIopSQxRmIiFRSiIkDCIQoxt0yyX/eHh4dHx8fHRyfjs7GJy0feZwSBi5sVyeT6ZzBYLM2va9tatmzu3dx977Au7d3Z3bt3uBl3JuVYFEKK0bRdiYGJiEublMp9Pzu7fv/+LX/7yk48/uXfv3nI+W1sbbqyvX7t+LcbQLxZaKxhRUjfodm/f/vVff+b27m7TDZmprqi6wwF3qFlRLVVzdTUDMYnE1KSmjSlxSBSk6brhxmY7GHBMd18/xiP739sBEf5/am5aS155//3333rzrXfe/snh0eHscmampeblcmlVJYRATMJNTINh17RJQgSgVXMpOedSsqqaQ4hxpZpqrVXVqjqcQACIiUAkLMxMDMDhqmZubgaAmIOIhBBDIEBXqjnc1Ryuag7HFQIRkxATE640qVnbWG/bVkSw4laq5n7Z93m5XJRSqyoAJmIWFgmSYmyfePJL3/zmN19+9ZXnfuPZ7Vs34Q5g9zsHeOTT//sa4BwCEbk7CARiIgPhCgNwIjzk6qp+MbncOzg4Ojw+Oj6ezxYg4hURYQKgqrVmV4WbuXnVoiX3y8V8Np9Oc+4BzGfT0+PjGOXxu3e7rp1OL+/du//2T3964/qNP/rrr1/b3PrxT9751YcfPzg46waj5776wp07d5rUxBRBzMISgxpqLQZjYS11sVyUkgGPKY0Gw+FoOBh0pe8PHxyenp1OLy4vzs/G4zPNpW3arWvXbu/uXN++ubG5ub6+PhqNmqbjIGbI/VLNGRRT6rph03YxJWJxNyIWIgBVa1UtmpnQNGkwHG5ubjz/7y9w5Yc7r+z+gz1cIXcDHHA4VnZfO8SVn/1Py8np8enJ8fR8nOdzaI1CKYQmtRJECERYIYawEBOIQ5SYkptdTqfnk/Ojo5Ozs7PJ2fl8PrOiRbXWmvs8m89BtLY2Gq6NVjY3N6/fuLG9vb2zc3vQdX2fVYupcoiDtokpEbEwiUjf9+Px+dHx4f7e3v7+/sHBwWRyXsuybeLWtWtt23hVwEmoa7uNja07u7vPPPP05tZWyaWqghiAASAhJoCrWVXNRUvRv/Y/v4Ir//g//yA2XYhJYkhdN9jYaLvBE390jiv7b96GO1aIcMXVTGvJfb9cvv/e+2+++eY7b799eno6nc1hJZfaL5ZmFkIQYmIKIXSDNqVETAC7WVHNOdeS1RxAYGEhAKpaixatpurmuOJwAnGQIMLEAByuaqZqZgCYOcQgIaQYCGyqZqZqpqpuKzB3OIGIWYQJREwA3Dw1cTQatW1LLERYcfVcS+5zn5e5L6pV1YhJiEnCioTui0888fVv/NbXvvbq8y999cX/yPDI/hs7EFLXUrIDMUYicncARMREBsIj7ER4yNW1+vn5xf29/YPD49PTs/l8YQZmEBHgqmZaS87mlQkrBLjWvuRaes3ZzJgxvbzYv78nTF984omuayaTyYcff/KjH719/fq1v/lH39m9eevnH/zqlx98/MsPPk1t9+JLL9994otr62sxpOrq5iTigKsBDmFzLTlXVSbElNZGw7ZtY5DL8/NPPvnkwd6D8enJeDI+n5zVql2TNjY2b926uX3z1o2b2zdu3Lxxc3ttbSM1yR39cllVAY4xDNphbJqQUpBITERMhBV7SKtWwGKMw2G3vrExGg6aJt79zhGu7L91G/+K737rAFf2v7cN1byYX44nk9Pj05Pj6fk4z+fQGoVSCE1qJYgQiLBCDGEhJhCHKDElN7ucTs8n50dHJ2dnZ5Oz8/l8ZkWLaq0193k2n4NobW00XButbG5uXr9xY3t7e2fn9qDr+j6rFlPlEAdtE1MiYmESkb7vx+Pzo+PD/b29/f39g4ODyeS8lmXbxK1r19q28aqAk1DXdhsbW3d2d5955unNra2SS1UFMQADQEJMAFezqpqLlqLVHSQxpdik2HQhJokhdd1gY6PtBhzj3ddP8Mj+93ZAhCuuZlpL7vvl8v333n/zzTffefvt09PT6WwOK7nUfrE0sxCCEBNTCKEbtCklYgLYzYpqzrmWrOYAAgsLAVDVWrRoNVU3xxWHE4iDBBEmBuBwVTNVMwPAzCEGCSHFQGBTNTNVM1V1W4G5wwlEzCJMIGIC4OapiaPRqG1bYiHCiqvnWnKf+7zMfVGtqkZMQkwSViR0X3ziia9/47e+9rVXn3/pq7d2boGwsvv6Azzy8Z9sOhBjJCJ3B0BETGQgPMJOhIdcXaufn1/c39s/ODw+PT2bzxdmYAYRAa5qprXkbF6ZsEKAa+1LrqXXnM2MGdPLi/37e8L0xSee6LpmMpl8+PEnP/rR29evX/ubf/Sd3Zu3fv7Br375wce//ODT1HYvvvTy3Se+uLa+FkOqrm5OIg64GuAQNteSc1VlQkxpbTRs2zYGuTw//+STTx7sPRifnown4/PJWa3aNWljY/PWrZvbN2/duLl948bNGze319Y2UpPc0S+XVRXgGMOgHcamCSkFicRExERYsYe0agUsxjgcdusbGy/+nR5Xfrjzyu4/2MMVcjfA3Q0OItp97RBX3v1f+snJ8cnhweXFJM/nZJoCN20z6gZBRFXd1EzNDQAzg0hi7NomhAjCYrEcjyfj8WQynoxPx6cnJ7PpXE1FpOu60Wi0sbXZdh2AQdtdu3F9Y319MBo1IRlMVXPOALq2DTERQVhYJOd8Ph5fTi9ns9nlxeX5xeTs9OTg4EG/XDRNiik1IUiUEOLW5ubdx+4+dvcLu7t3Uorn5+d9LjFGZlEQEYeYiMWBqpZLXRb9t/+H53Hl//1Pf9G2XUhtSCkNusHaetN1T/zROa7sv3kb7lghwhVX01pK7heLxXvvvffnb77147d/cnJyPLucVq2l5FoKgCYmkeDwGKRJiaMQMQAHTC3nXFXNjJljEGIhwMxLLVprVXV3AG4PARAWlhUmkMNVzVTNzOHMHGMMMcYQmcmKqmlVNdWq6mamRkxMTMxBhIQJBMDhKcZuMEgpCTMRiAWAm1a1WnLJNZecS6m1ujlAIjGk9vHHn/j613/z1b/06suvvPzyHxMe2fuHt0iompXaA4ghkrC7AyAiJjIQAHcnEAMEAqDqXm08ubh/f+/o6PhsPJnPF6pwmLur1lpqKbmW7KoiFIRFBHDVLCKjwSCKqJbj48MPfvHL5Xy+vb2dkszmi737ez/72XtN2/7ON//yzu2d6Wyxv3/47s8/5Ni+9MqrTz755NbWtRjDfLkotQJEJEFEUggSWLBCLDGGGGMKwV2Xi8XBg/1fvP/+3t7e7PJiMZ/lnKsqM1JIg0G7trG1vb29e+cLjz/xxK1bt9bWN5hkvlzUUquqsKSmaZs2tV3zUEss7gZ3AOaqpgBilK7rNjbWB8NB16W7rx/hyt6btwEQAfDdbx3gyv73bsIsL+fT8XhycnxyeHB5McnzOZmmwE3bjLpBEFFVNzVTcwPAzCCSGLu2CSGCsFgsx+PJeDyZjCfj0/HpyclsOldTEem6bjQabWxttl0HYNB2125c31hfH4xGTUgGU9WcM4CubUNMRBAWFsk5n4/Hl9PL2Wx2eXF5fjE5Oz05OHjQLxdNk2JKTQgSJYS4tbl597G7j939wu7unZTi+fl5n0uMkVkURMQhJmJxoKrlUpdFSykODjFISm3bhdSGlNKgG6ytN13HId79zgke2f/eDohwxdW0lpL7xWLx3nvv/fmbb/347Z+cnBzPLqdVaym5lgKgiUkkODwGaVLiKEQMwAFTyzlXVTNj5hiEWAgw81KL1lpV3R2A20MAhIVlhQnkcFUzVTNzODPHGEOMMURmsqJqWlVNtaq6makRExMTcxAhYQIBcHiKsRsMUkrCTARiAeCmVa2WXHLNJedSaq1uDpBIDKl9/PEnvv7133z1L7368isv376zCwbgu68f4JGP/q8NADFEEnZ3AETERAYC4O4EYoBAAFTdq40nF/fv7x0dHZ+NJ/P5QhUOc3fVWkstJdeSXVWEgrCIAK6aRWQ0GEQR1XJ8fPjBL365nM+3t7dTktl8sXd/72c/e69p29/55l/eub0znS329w/f/fmHHNuXXnn1ySef3Nq6FmOYLxelVoCIJIhICkECC1aIJcYQY0whuOtysTh4sP+L99/f29ubXV4s5rOcc1VlRgppMGjXNra2t7d373zh8SeeuHXr1tr6BpPMl4taalUVltQ0bdOmtmseaonF3eAOwFzVFECM0nXdxsb6i38348oPd17Z/Qd77lghd4O7w+Agwu5rR7jy/nfz+PTk5OjgYnyW53MyTSkMmmY4HAqL1mJaSy0wAxOIiDkEiSnFkEIKpj6dzeaz2Ww6G5+ODw+PptM5YF3Tbm5ujtbXR6MRMed+KRLWNtaHg0FKSURAZKq57wGkpokxCgkJEXHJ+fzifLFYlFqtVtVycX6+/2B/enlprszUNSk1bdukra1rdx9/7Mb29vrauplNJpNStU2JQgCYWEJMEgQkataX2mf9q//9c7jy//y9nzdd17RNbNtmMBysraW2e/xvTHBl/3s7+NeI4O5mpjX3/WKxeO/d9/70rbfe/vGPj4+OLs8vi2ZTNTNmblITRAAwg0WEhRggBlDVaslVDeYcJKUURVjEAa21lJJLMVU1c7UVAMxMwgRiJjN3uKnairsQhxhjiikmAtWatZrDXa2qupnDCcRBggiLCBMRuxsAJkltihI5iBCJBFwxt1JKzrnv+1xKycXMADDH1A7v3PnCCy+88MrXvvaNlf+sxSN7//AWhAxWSnYgSoAQ3AFaYSID3LFCAIMIBEDVtdfx+fm9+/uHh4fj8WQ+X5q6utqK1nLFSja3IBxjaGIkJod2TbO1sdU2SS0f7O//9J13Jmdnbdcwc+6XJyenn3zyaUrhq889e/PWtjsdHp29+/MPIemrzz//pS8/defOY02bLi4ul7k3c+HQdW1MKQSRIMwSU+jaVljUdDGbnp6c3Pvs0/d//v7J0SHBCWBGLWU6mzHx5uZ6NxjFlLY2r+1+4c7O7p2d27tdNyx5WaoCzEwxxrTSdl3btW3HIm4AjAjuXq2AOAYZDAabGxuD0aBp4t3XD3Hl3vduM4EIcN997QBX9r93E2a5X8zH4/HpycnRwcX4LM/nZJpSGDTNcDgUFq3FtJZaYAYmEBFzCBJTiiGFFEx9OpvNZ7PZdDY+HR8eHk2nc8C6pt3c3Bytr49GI2LO/VIkrG2sDweDlJKIgMhUc98DSE0TYxQSEiLikvP5xflisSi1Wq2q5eL8fP/B/vTy0lyZqWtSatq2SVtb1+4+/tiN7e31tXUzm0wmpWqbEoUAMLGEmCQISNSsL7XPmnOuBhIJMTVd17RNbNtmMBysraW2oxDvvn6MR/a/t4MVIri7mWnNfb9YLN57970/feutt3/84+Ojo8vzy6LZVM2MmZvUBBEAzGARYSEGiAFUtVpyVYM5B0kpRREWcUBrLaXkUkxVzVxtBQAzkzCBmMnMHW6qtuIuxCHGmGKKiUC1Zq3mcFerqm7mcAJxkCDCIsJExO4GgElSm6JEDiJEIgFXzK2UknPu+z6XUnIxMwDMMbXDO3e+8MILL7zyta994xvfeOzxu5IYhDuvH+D5zHDHAAAgAElEQVSRj/9k04EoAUJwB2iFiQxwxwoBDCIQAFXXXsfn5/fu7x8eHo7Hk/l8aerqaitayxUr2dyCcIyhiZGYHNo1zdbGVtsktXywv//Td96ZnJ21XcPMuV+enJx+8smnKYWvPvfszVvb7nR4dPbuzz+EpK8+//yXvvzUnTuPNW26uLhc5t7MhUPXtTGlEESCMEtMoWtbYVHTxWx6enJy77NP3//5+ydHhwQngBm1lOlsxsSbm+vdYBRT2tq8tvuFOzu7d3Zu73bdsORlqQowM8UY00rbdW3Xth2LuAEwIrh7tQLiGGQwGGxubDz/d5a48sOdV27/b/fNiQlkpgAIDjgcu98+wpWf//16fnZ6fHRwMT7rp1MyTSl0bTtou8BUanUtagpAmEkETERMBBAxGIABZubqy8Xy4vKy5BJEUtOMBsOYohv6nOfzmZulpmmbpmnbIKJmplpqJaKuaWNKQQSAAzkvp5fTxXKR+0xMg0EH+Hw+WyznuV+qahBp22Y4Gq2trI9SimrQUvq+d0dKKUhCCCKRg5AIs6ijVO2z/pX/7hlcees/+WnXDZtB1wxGg+GwW1uLbXf3D09xZe+NW7hCTFgxB9zdSs6L+fzdn737p2+99cN/+aODwwfn5+dasrlHCSGGFKKwgAGDQ4mZRZhJDaaaS3EzADGGtu2aJqXUEJNWzTnP54s+9zlnN2MRApkbVsxxxeG24u7mIhziQylGGHLuzYxFCORuD7kLcUxRWFiEGWZwOAACMYNFokSJIQgDMLWqmnPu+z7nXEo1U3cAFFIzbEfXt29+4e7dl1588Xd/7/f+nf/qJh7Z+4e3SMjJVdXhBALDzYmJiEHk7nCAiAAGEQhArVb6OhlP7t3fPzg4OD09m84WVc3NADe3Fa3VaoF7jNKk2DRtjMKEpk3rw1HXJhE5PHzwkx//+MH+A3fVmvMyT2fT8WQ8Gg2f/crT6xsb0/niwcHJhx99XiFPfulLTz399LPPfnV9Y218fj6bz0tRERkOR01KIJBQCNKkNOiGgM3n8/Hpyf179z797NNPP/64Xy6uX9vcWB8NB910Nv30k09SiM88++tN2+3t7y/mfWy6nds7Tz39le3tbTVEkabtYgjEHEJITRtTijGJCABaYXbXWgsIMaXRcLi1tTkYDmKQx14/wJW9N3eIyN0B3HntAFf239gGtCyX84uL87PT46ODi/FZP52SaUqha9tB2wWmUqtrUVMAwkwiYCJiIoCIwQAMMDNXXy6WF5eXJZcgkppmNBjGFN3Q5zyfz9wsNU3bNE3bBhE1M9VSKxF1TRtTCiIAHMh5Ob2cLpaL3GdiGgw6wOfz2WI5z/1SVYNI2zbD0WhtZX2UUlSDltL3vTtSSkESQhCJHIREmEUdpWqftc+5FHWAY+i6YTPomsFoMBx2a2ux7STInW8f4ZG9N24RE1bMAXe3kvNiPn/3Z+/+6Vtv/fBf/ujg8MH5+bmWbO5RQoghhSgsYMDgUGJmEWZSg6nmUtwMQIyhbbumSSk1xKRVc87z+aLPfc7ZzViEQOaGFXNccbituLu5CIf4UIoRhpx7M2MRArnbQ+5CHFMUFhZhhhkcDoBAzGCRKFFiCMIATK2q5pz7vs85l1LN1B0AhdQM29H17ZtfuHv3pRdf/N3f+72nvvLl2DVBePf1Azzy+Rs3HE4gMNycmIgYRO4OB4gIYBCBANRqpa+T8eTe/f2Dg4PT07PpbFHV3Axwc1vRWq0WuMcoTYpN08YoTGjatD4cdW0SkcPDBz/58Y8f7D9wV605L/N0Nh1PxqPR8NmvPL2+sTGdLx4cnHz40ecV8uSXvvTU008/++xX1zfWxufns/m8FBWR4XDUpAQCCYUgTUqDbgjYfD4fn57cv3fv088+/fTjj/vl4vq1zY310XDQTWfTTz/5JIX4zLO/3rTd3v7+Yt7Hptu5vfPU01/Z3t5WQxRp2i6GQMwhhNS0MaUYk4gAoBVmd621gBBTGg2HW1ubz/17M1z54c4rN797T4QeMlMARA4H3He/fYQrv/j79Xx8dnJ4cD4566dTaG1SaFNq25aZrFZVdTciBBEwE5MD8BWAQCyBhUSYxKr2fQaQVpqmbVoAy/liPpteTqelFCKKMTapDUHcTVVrrUySmhRjEBEA5l5K6Zd93y9LKTHFzc2tQZdAKCXPFtNSCgMxxtFo1DSJg7jpcrkspVY1IY4xSQgskSSEECkIsyioqPe5/JX/9iu48uYfv9N0XTscDYajbm1tMBrFtn3sO6e4cu+NmwAxQExYMQccQK1lPp//4r33//zP/uxHP/rR3v3988k45x7wGKOEkDiQMIEcrloAhBiZSc1NrdTqamCKEpq2ads2NUlEzDznfjFfzBeL5XLpZjFGIlI1c3MzNwfgcHN3c8CJOUqIKcYYAJRc3FwkEJOZwd3NJYaUUowhiACoqq6uZrgizBJDDCIsxFxVSym5z6XklVqquQEgYg6pawfr61s3bmy/+NILf+33f/9v/I9fwiP3/uEtFiKGu5m7wwG4OTERMUDujhUiAjFAIAC1ar/I47PJvfv7Bw8OTk/PLqdzVa1mTG7uMHc30wp4DKFpUte1TQwk1KQ4bLquS02bzk7P3nv3Zw/27s8Xi36x6POy5L70ebQ2ePzu3ZjS2dn5g+PTvQcnCrl9e/epp5966eWv3bixfTmb9susqhLCsBtKCmbOBA4SgzSpMa0XF+cHBw8++eijzz77bH/vfgzy9JefvHnjekzx5OT4p++8k1L8zd/6rdFw7cOPPj46Op71y2tbN5597rmdnR1mSU07HI6alACEkFJqQopEJCIsIsxE5O5FMxGllNbWRltbW8Nhx4Ef+/YBruy9uUNE7g7gzmsHuLL/xg24luVycXl5Pj47OTw4n5z10ym0Nim0KbVty0xWq6q6GxGCCJiJyQH4CkAglsBCIkxiVfs+A0grTdM2LYDlfDGfTS+n01IKEcUYm9SGIO6mqrVWJklNijGICABzL6X0y77vl6WUmOLm5tagSyCUkmeLaSmFgRjjaDRqmsRB3HS5XJZSq5oQx5gkBJZIEkKIFIRZFFTU+1z6vi9ZKyxIbLquHY4Gw1G3tjYYjWLbsoQ73z7EI3tv3CImrJgDDqDWMp/Pf/He+3/+Z3/2ox/9aO/+/vlknHMPeIxRQkgcSJhADlctAEKMzKTmplZqdTUwRQlN27Rtm5okImaec7+YL+aLxXK5dLMYIxGpmrm5mZsDcLi5uzngxBwlxBRjDABKLm4uEojJzODu5hJDSinGEEQAVFVXVzNcEWaJIQYRFmKuqqWU3OdS8kot1dwAEDGH1LWD9fWtGze2X3zphb/2+7//3G88OxgNQxPvvH6AR/bevGnuDgfg5sRExAC5O1aICMQAgQDUqv0ij88m9+7vHzw4OD09u5zOVbWaMbm5w9zdTCvgMYSmSV3XNjGQUJPisOm6LjVtOjs9e+/dnz3Yuz9fLPrFos/LkvvS59Ha4PG7d2NKZ2fnD45P9x6cKOT27d2nnn7qpZe/duPG9uVs2i+zqkoIw24oKZg5EzhIDNKkxrReXJwfHDz45KOPPvvss/29+zHI019+8uaN6zHFk5Pjn77zTkrxN3/rt0bDtQ8/+vjo6HjWL69t3Xj2ued2dnaYJTXtcDhqUgIQQkqpCSkSkYiwiDATkbsXzUSUUlpbG21tbf36377ElR/uvHLzu/dE6CEzBUDkcMB999tHuPLz/7Wcj8+Ojw4uxmf9dAqtTQopxSY2QlBTuMEcDCJiYmP8ayJCLDEEELtC3dw8SGjbtmmamJKrzufz6eX08uKi73s3IyIWCSsSYF5KdjiHEERABICJzU1VzQzuXddubW0Oh8MYxVwX/aLkbKogCiEGIZBX1VpKqQpzkLAIixAJS4gxckwSgoKras76O//NU7jyj/74nbYbdMPRYDQajkbNcBjb9u4fnuHKvTduMRMBIMKKOx7yWkq/WH74wS//4i/+8Ts/eefevc/HZ+PFcmZqzBxEmEQYBFaYlQpGkEDCqmbm7ubmAFgohBhCSE0KEgCUUvvlYrFczGcLM0tNYmJzUzNXWwGgbm4O+AoRBxGJIaVERG4Ggwibu6maGYFiit2ga5o2iLhZLrlWLbW6GxELk0gMIhyEiNy0VM19rjWXUkupZoqHCBDiMByOtm/uvPjii9/6gz/42//7c3jk8z/ZlijEAHzFAIfDHaAVgNwdK/QQOxEeqqUu5vn09Oze/f3Dw8Ozs8l0OsulqirggGPFVpQJMUjTpCY1MQoTxShNil3bDIbDxWx677NPDw8Pp5cX8/k856XWaq4ppY31tVrqgwfHp5Pzi1mmmK5d337y17708ksv3769644VFokhxZRAqFUdFkVADNd+sZhcnD/Y2/vwg1/ev/f58fHR9a2NV1995eaNG9P55acff/yDH/ygadvf/b2/euvmzoPD4+Pjk6OT06btfu3LX96+vh1iSE07HI6a1BBziKlrOw4BgDDHmESECO5aVUmoadrRaHBta3Mw7FjksW8f4MremztE5O4A7rx2gCv7b9yA1bJczi8vz8dnx0cHF+OzfjqF1iaFlGITGyGoKdxgDgYRMbEx/jURIZYYAohdoW5uHiS0bds0TUzJVefz+fRyenlx0fe9mxERi4QVCTAvJTucQwgiIALAxOamqmYG965rt7Y2h8NhjGKui35RcjZVEIUQgxDIq2otpVSFOUhYhEWIhCXEGDkmCUHBVTVnXSyXOVd1cJC2G3TD0WA0Go5GzXAY21ZCvPPtQzyy/70dEGHFHQ95LaVfLD/84Jd/8Rf/+J2fvHPv3ufjs/FiOTM1Zg4iTCIMAivMSgUjSCBhVTNzd3NzACwUQgwhpCYFCQBKqf1ysVgu5rOFmaUmMbG5qZmrrQBQNzcHfIWIg4jEkFIiIjeDQYTN3VTNjEAxxW7QNU0bRNwsl1yrllrdjYiFSSQGEQ5CRG5aquY+15pLqaVUM8VDBAhxGA5H2zd3XnzxxW/9wR/8xgu/sX5ts22bO985wCP7b91ydwMcDneAVgByd6zQQ+xEeKiWupjn09Oze/f3Dw8Pz84m0+ksl6qqgAOOFVtRJsQgTZOa1MQoTBSjNCl2bTMYDhez6b3PPj08PJxeXszn85yXWqu5ppQ21tdqqQ8eHJ9Ozi9mmWK6dn37yV/70ssvvXz79q47VlgkhhRTAqFWdVgUATFc+8VicnH+YG/vww9+ef/e58fHR9e3Nl599ZWbN25M55effvzxD37wg6Ztf/f3/uqtmzsPDo+Pj0+OTk6btvu1L395+/p2iCE17XA4alJDzCGmru04BADCHGMSESK4a1UloaZpR6PBta3NZ/7WFFd+uPPKze9+LsK0YqYAiBwOwHdfO8KVn383n4/HR0cHF2dn/ewSWpsmNjGkmJgYru4GM4evmDsIIAJIgoQQJUgICYCquUOIY0xd16WUQpBadTZ9aH45XSwXWnXF3YNI0zQASinuziLE5O4ACRPxFTAJtW23sb42Whs2bSJGKbnUUkt1VXOYq5tWU1czdQMYBBEQA8QcY5NiTBKTkRTVkvNv/9dP4co/+uOftoPhcDgarq11w2EaDmLT3v3DU1zZe+MWmFbwbzjcaymL+fzjX/3q+9///s/e+dm9+/fPTk8Xi5mpEoFZAgsRE5OZmioAFgFQVc2cmAhwgPBQEAkxigQRVrV+uVgsF/PZwsxSk5jY3NTM1VYcbu5uZmoOYxYWiRJiisxCRAwQQc1N1c1JuIlpOBy2bctB3K3vcymllqzmAISJRYI8BGK4qWrJtWgppWqpVdXhMDKgKgaD4fb2rRdffOFb33rt7/4fL+GRz/9kW4QpEOAAHDD4CgAigpO7Y4UeYifCQyXXxWx5enL22b29w6Pj8Xg8n836XEpVuIEgRICbGxOiSIyhiUmiEEGYYghNisOuraWMx2fjyfhiMp5NLxeLRa0FcCIwS7/Mp2dn09mygGPqRpubj9994vnnn9/Z2WWREEQkSQgxCICqToQUg7vnnKeX5yenx/t79z/56KODvb3JZLy9ff0bX3/1+vWto+PjDz/44Af/4p8HkX/rt3/nsbt3VTGZXN5/sM8SHn/ii9euXY8xNk03GHQxJBZJMTVdF0JQc2FpmsQiRHDATUmka9NwNNra3BgMWxZ57NsHuLL35g4RuTuAO68d4Mr+GzegtSwW8+nl+Xh8dHRwcXbWzy6htWliE0OKiYnh6m4wc/iKuYMAIoAkSAhRgoSQAKiaO4Q4xtR1XUopBKlVZ9OH5pfTxXKhVVfcPYg0TQOglOLuLEJM7g6QMBFfAZNQ23Yb62ujtWHTJmKUkksttVRXNYe5umk1dTVTN4BBEAExQMwxNinGJDEZSVEtOc8XOddSzUVCOxgOh6Ph2lo3HKbhIDathHDn24d4ZP/N2/g3HO61lMV8/vGvfvX973//Z+/87N79+2enp4vFzFSJwCyBhYiJyUxNFQCLAKiqZk5MBDhAeCiIhBhFggirWr9cLJaL+WxhZqlJTGxuauZqKw43dzczNYcxC4tECTFFZiEiBoig5qbq5iTcxDQcDtu25SDu1ve5lFJLVnMAwsQiQR4CMdxUteRatJRStdSq6nAYGVAVg8Fwe/vWiy++8K1vvfb8i1+9fuN6O2jv/OEhHtl/6xYABwy+AoCI4OTuWKGH2InwUMl1MVuenpx9dm/v8Oh4PB7PZ7M+l1IVbiAIEeDmxoQoEmNoYpIoRBCmGEKT4rBraynj8dl4Mr6YjGfTy8ViUWsBnAjM0i/z6dnZdLYs4Ji60ebm43efeP7553d2dlkkBBFJEkIMAqCqEyHF4O455+nl+cnp8f7e/U8++uhgb28yGW9vX//G11+9fn3r6Pj4ww8++MG/+OdB5N/67d957O5dVUwml/cf7LOEx5/44rVr12OMTdMNBl0MiUVSTE3XhRDUXFiaJrEIERxwUxLp2jQcjbY2N575W5e48sOdV25+93MRppWqlQkrRIBj97VDXHn/u3lyenpycnRxdtbPLqG1aWKTYpOaSAKomWqtVWupVVUNTsyBJaYYUxNTSjEColUBSAwpxabpQhAmyjnPZ/P5bNYvFv2yzzmXknMuTNS2DTPXaswkIRCRmcIB5hhCTDFIAFNMcTDoukE3GHQhBnfVqlprLjnnUmtVLaaOFSYmBgmumIMlpKaNKXFsQKKmfa6//V9+CVfe/I/f64bDwWg0Wltrh6N20HFqHvvOMa7svXGLmEAEd6wQAQ733PfTi8uPfvXBP/sn//Tdd9+9v7c3PhsvFwuzwixBJEgUIWJx91IL4MKk5n0ubh6CMBGYYWbuAFgkhiBB3LxfLhfLxWw2N7OmaZjY3NTM1VYcrmqmqmYOJ1CMQURCjEFEWIQJgDuqKoAg0jRpMBw1TRODmKP0OdeyYqpmSmCWFRIREANws1KqqRYtWrWqmpq5W/WsaNvBzZs3Xnjhpdde+4P/4P/8Gh75/E+2JQoxAAfgeMjgABhkvoKH6CF2IgCOkstsujg+Pr13b+/g6Hgymcxn80Xfa1W4gSAiTAAcQBQOQaIEFgZcGEwSAwsz4FrLfDG7mEwuLs4vL6fL5aLUYlq1qKkVVwNzSLEZDEfrO7d3n3r6mRs3roMAYiEhEiIJgYglpdg0DeD9cnF6enLv888e7N8/PDw4Oz2Znk+2Njee/+qzo9Hw+Pjo448++slP3tZavvLMM48/8eSt27vmdH9vn0geu3t3a/NaTKlpm6ZpY0gAYkpt17GIqRJL13YhCBFWDJAgXdetjQYbmxtd17LwY98+wJW9N28RkZoz4c5rh7iy/8YN11oWi/n0cnJ6enJydHF21s8uobVpYpNik5pIAqiZaq1Va6lVVQ1OzIElphhTE1NKMQKiVQFIDCnFpulCECbKOc9n8/ls1i8W/bLPOZeScy5M1LYNM9dqzCQhEJGZwgHmGEJMMUgAU0xxMOi6QTcYdCEGd9WqWmsuOedSa1Utpo4VJiYGCa6YgyWkpo0pcWxAoqZ9rotFzqVU9RBCNxwORqPR2lo7HLWDjlPDIne+fYBH9t+8DXesEAEO99z304vLj371wT/7J//03Xffvb+3Nz4bLxcLs8IsQSRIFCFicfdSC+DCpOZ9Lm4egjARmGFm7gBYJIYgQdy8Xy4Xy8VsNjezpmmY2NzUzNVWHK5qpqpmDidQjEFEQoxBRFiECYA7qiqAINI0aTAcNU0Tg5ij9DnXsmKqZkpglhUSERADcLNSqqkWLVq1qpqauVv1rGjbwc2bN1544aXXXvuDF1584fr29XbQ3fnDQzyy/9YtAI6HDA6AQeYreIgeYicC4Ci5zKaL4+PTe/f2Do6OJ5PJfDZf9L1WhRsIIsIEwAFE4RAkSmBhwIXBJDGwMAOutcwXs4vJ5OLi/PJyulwuSi2mVYuaWnE1MIcUm8FwtL5ze/epp5+5ceM6CCAWEiIhkhCIWFKKTdMA3i8Xp6cn9z7/7MH+/cPDg7PTk+n5ZGtz4/mvPjsaDY+Pjz7+6KOf/ORtreUrzzzz+BNP3rq9a0739/aJ5LG7d7c2r8WUmrZpmjaGBCCm1HYdi5gqsXRtF4IQYcUACdJ13dposLG58ZV/9wJXfrjzyq2//znTlaoVABOIAMfua4e48v5388V4fHx8cDE+66dTaG2a2KaYUhOEyeGmpZZa8kpdcWemEGNMqUlNjIElMpMaRDhKik2KKQUmdy85z+eLfrksfe6Xy+ViMZvPZ5dTd2vaNkoAUwwhpQSg1mruzBybNBgMmyYxCUeJQZomdYMupkCAw6tqyblfLvuSa61uICYhWQExAMMKMcfUNKFpJSZAzDVn/eZ/8QSu/Nnf+6AbdIPR2nBtrR0Om66TmO68fogre9/bIVwxxwoTVsyX/eJ8MvngF7/8p9///rvvvvvgwYPz80nJ2c1TDCHGJEEkcBB3yzm7GwhadL7sAYsxiQgz24o7zEhEmEWCu/Z9Xi4Xs9m8qjapEWF3VzNXW1E3N1P9/7iCs1/Ls+s+7N+11t779/udc+65U1XX1NXs5hRS3WwO4KuQIBDVNklVgQkSJHlKIth/Q14COfZLEDh5jiUHFhQECBIkjSYBS4Ioi4xhIA6IqCdO6ibdNdyh7njumX7D3mutnLqtkmN/PkXN3Y2IAwuLxBhEQpQQAhMxgKLKRMycYqpHTVWlIGKOkoeiZqpmWlThYBEhZiFiwTU33ShFcymmWlRNvaiX4nXd3Lhx82tf/+p3v/s7f/d//iZeePJ/vMSBiQE4AMdzBgfAIPPnAAKREMGJADiGPi/nq7Oz88dPnp6cnF7Orlarddv3WgrcmUlCYGLAmUGEIBxZwIAbE4kIgwBjphiDlbJcLZaLxXyx6No256FosaIhhLoZc5BBwZKqptnbv3H/lVenW5Mhq5uaExMTS4qpqlKqqrqKcHR9e3VxcXB0eHp6fHF2dnF+enF2Om6qL37x86NxM5/Nnjx5/Iuf/7Tvupfu3Hn55fuvvPpaTM3FxVVI6f4rr+zt7sWNkGJKzAKAQ0xVJcxFNYhUdROCAGAiMMcYmlEzmYyn21tNXbPw/YfHuHbw/VtEUMPG/YfPcO3w7RuuJbftermYX16enh7PLy/65RJaqirWKaZUBWFyuGkuueRho2y4M1OIMaZUpSrGwBKZSQ0iHCXFKsWUApO752FYr9u+63I/9F3Xte1qvV4tlu5W1XWUAKYYQkoJQCnF3Jk5Vmk0GldVYhKOEoNUVWpGTUyBAIcX1TwMfdf1eSiluIGYhGQDxAAMG8QcU1WFqpaYADHXYdB1Nww5m3mIqRk1o8nWeGurHo+rppGYSOjug2O8cPjObZhjgwkb5l3fXs1mf/WLX/5fP/rRhx9+eHR0dHU1y8Pg5imGEGOSIBI4iLsNw+BuIGjWddcDFmMSEWa2DXeYkYgwiwR37fuh69rVal1Uq1SJsLurmattqJubqRY1dzciDiwsEmMQCVFCCEzEAIoqEzFziqkeNVWVgog5Sh6KmqmaaVGFg0WEmIWIBdfcdKMUzaWYalE19aJeitd1c+PGza99/avf/e7vfO0bX9u/sV+P63v/wTO8cPiDWwAczxkcAIPMnwMIREIEJwLgGPq8nK/Ozs4fP3l6cnJ6ObtardZt32spcGcmCYGJAWcGEYJwZAEDbkwkIgwCjJliDFbKcrVYLhbzxaJr25yHosWKhhDqZsxBBgVLqppmb//G/VdenW5Nhqxuak5MTCwppqpKqarqKsLR9e3VxcXB0eHp6fHF2dnF+enF2em4qb74xc+Pxs18Nnvy5PEvfv7TvuteunPn5Zfvv/LqazE1FxdXIaX7r7yyt7sXN0KKKTELAA4xVZUwF9UgUtVNCAKAicAcY2hGzWQynm5vfek/m+PaT25/89Y/esy0Aco5E4NpA3DcffAM137+P+b55cX52elidtEul9BSB0kpVinJBsHcteSch67vcynuBqIYo4QQY2QWEMASQpAQowRJMYbEBFW1nPNQhjxY1q5bz+eLq8vL89OzPg9N01QpxRhTVdVVTUxd15tbEGlGo53dnWY0CiEQsUOJpUpBokhgInLzUnLf90POWoopRISYAkcSAhhMTFFCCCmFmDgkMKtazvqbf/8zuPbH/9WvR6NxM55MJlvVaBRHdQjh7sNjXDt85zaI4O5mcCdiPGften1+cfGzD3/65z/84U8/+PD09GS5WLori6SUqphSVaUgLGJq/dCrFjMfcu7a1syruooxighAgONfIzPt+77tuna9LqopJWE2c3Nze07VTLWYuho2GEwSgoQQhENKIUgQFjBgeI4RRFJKEiIRATBVAERwh5magwnMQsxCxMK4ZmpqmotqLlnV1EpxVdR1s79/482vvfnd737nd//wG3jh4P+8RUJgAO7YcAMYhGvmzwFExExEDjjc0HJYSsEAACAASURBVPfDark6PT0/eHLw7OT0YjZbLFd935eiTGDhKiUiBszhAARgJsABF+YYIxFMNQo3TSOB89D3fbdet8MwuCkAYWrG4/39G+Z0cnbe9kNM9Xhra2//ZpCwXC2HvrgBRDHEqq7Go3FMKQjcLJcybHSr+Xx28uz46ODpo0/+FcM+8+orO9vTosPF2emvf/XRet3u7O5u7+7u7OxXzRiQ7d3d1157bXd3D5AQRDgCUDciDjERk5sLS1WnEIK5CwsHqqp6NB5NJuPJ1riuKha+//AY1w5/cAtAKU6M+w9PcO3w7ZuwnNfterWcX16cn50uZhftcgktdZCUYpWSbBDMXUvOeej6PpfibiCKMUoIMUZmAQEsIQQJMUqQFGNITFBVyzkPZciDZe269Xy+uLq8PD896/PQNE2VUowxVVVd1cTUdb25BZFmNNrZ3WlGoxACETuUWKoUJIoEJiI3LyX3fT/krKWYQkSIKXAkIYDBxBQlhJBSiIlDArOq5az9UHJRc3AMo9G4GU8mk61qNIqjOoQAorsPjvDC4Tu33QzuRIznrF2vzy8ufvbhT//8hz/86Qcfnp6eLBdLd2WRlFIVU6qqFIRFTK0fetVi5kPOXduaeVVXMUYRAQhw/Gtkpn3ft13XrtdFNaUkzGZubm7PqZqpFlNXwwaDSUKQEIJwSCkECcICBgzPMYJISklCJCIApgqACO4wU3MwgVmIWYhYGNdMTU1zUc0lq5paKa6Kum7292+8+bU3v/vd77z5ta/uvbTfNPXd7x3jhcMf3HJsuAEMwjXz5wAiYiYiBxxu6PthtVydnp4fPDl4dnJ6MZstlqu+70tRJrBwlRIRA+ZwAAIwE+CAC3OMkQimGoWbppHAeej7vluv22EY3BSAMDXj8f7+DXM6OTtv+yGmery1tbd/M0hYrpZDX9wAohhiVVfj0TimFARulksZNrrVfD47eXZ8dPD00Sf/imGfefWVne1p0eHi7PTXv/povW53dne3d3d3dvarZgzI9u7ua6+9tru7B0gIIhwBqBsRh5iIyc2FpapTCMHchYUDVVU9Go8mk/Fka/zv/KdXuPaT29+88/uPiQgA5ZyJwbQBOO4+eIZrv/hHZXE1uzg/Xc4u2+USJccokUMIEphYGA43zWUoOWdVdwORhMDMBCjcVCGhrpsUK44xBpEQhKDqMHU1UzfV9Wp5fnr+7OTZwdOnq3XbVKl6rq7qatQ0ANZd5+4pxq3p9Nad29s7u01dgVhNAWUmEhYRIsBRVHMecs6lFDcDmEAhCEkUERIJIYWQOIhIohAANkdW/c2/dx/XfvhfP21G49Fo3Iwn9aiROkkIdx8c4drhO7dBBHc3gzsR4zlbrdcXZ+fvv//en/3Jn37wwQcXF5dd2zJZCLG6ljYkioiqtv0656yqQx66bnDzqq5SSjFGEQHhOYepFrWch34Yuo11q25RAgm7O8zNTN1M1UyLqpsDIBCLCDOLpBhTijEEFiFid8OGgYQDCwnhUw4QokRicnMAxEQEYhFmJiYhAO5w06JW8lCKlqKqyIoqNXv7e29+9c3vfPvbv/uHX8cLh2/fhhDIAXdsuAEMwjXz5wAiYiYihzvMMPTDerk6P7t4+uTg+OTk/Pxyvlj2Q29mzBRCqKuKmc3NVN1tgwGCA2DhIEJMMIshjCejKkbANrIqzEAkwjGG8Xi8u7uXiz45PFos1yQSUxqNtuBYrVa5FKIgIlFiVaW6GQXhXIaSVTUzIQZu29XZ2cnhk8e/+vijvmtv3tjb3p40ddV162dHB13f1aNxjMkR6maye+PG3bv3Xnvts9Pt7VLMnYMIgKKKDRIQ4BCWlBIHcTcWjjHUdbO1NRmNx5PJqKqiCN97cIxrhz+4BaAUJ8b9hye4dvj2TVguXbdeLhdXs4vz0+Xssl0uUXKMEjmEIIGJheFw01yGknNWdTcQSQjMTIDCTRUS6rpJseIYYxAJQQiqDlNXM3VTXa+W56fnz06eHTx9ulq3TZWq5+qqrkZNA2Ddde6eYtyaTm/dub29s9vUFYjVFFBmImERIQIcRTXnIedcSnEzgAkUgpBEESGREFIIiYOIJAoBYHNk1T5rUQM4xNCMxqPRuBlP6lEjdZIQANx9cIQXDt+57WZwJ2I8Z6v1+uLs/P333/uzP/nTDz744OLismtbJgshVtfShkQRUdW2X+ecVXXIQ9cNbl7VVUopxigiIDznMNWilvPQD0O3sW7VLUogYXeHuZmpm6maaVF1cwAEYhFhZpEUY0oxhsAiROxu2DCQcGAhIXzKAUKUSExuDoCYiEAswszEJATAHW5a1EoeStFSVBVZUaVmb3/vza+++Z1vf/vNr7+5d3O/aZq73zvCC4c/uOXYcAMYhGvmzwFExExEDneYYeiH9XJ1fnbx9MnB8cnJ+fnlfLHsh97MmCmEUFcVM5ubqbrbBgMEB8DCQYSYYBZDGE9GVYyAbWRVmIFIhGMM4/F4d3cvF31yeLRYrkkkpjQabcGxWq1yKURBRKLEqkp1MwrCuQwlq2pmQgzctquzs5PDJ49/9fFHfdfevLG3vT1p6qrr1s+ODrq+q0fjGJMj1M1k98aNu3fvvfbaZ6fb26WYOwcRAEUVGyQgwCEsKSUO4m4sHGOo62ZrazIajyeT0Rf+k0tc+8ntb975/cdEBIByzsRg2gAcdx88w7WP/rEv51eXF+fLy4t2tfQ8RCHCcwxIECEGDPCiCrgDoOfMbCgl59wPPUkYb23VdUMsIYYokYUEzAQmgrmpLa6ujg6PHj9+/Otf/fpqdiVRqpBilZq6quoaRG3bAmia5ubNG6++9tpLt+/sbG/HFFS1uLqpwx0wAG5QV9Wcc9FSiqoWgEQkhMQxxJCqupaQmAUsxMGJ1WGG3/y9u7j2439w3myMxvV4lKqKU2Dhew+Pce3wndsggjvMscGEDfO2XZ2fnb333nt/+k//+IMPPpjNroau48AxhKpKKVVVTCEIs5iW1XrdD13ORUvORQGkmFKVqqqOMYoIMZtZzrnvu67ru3XbD/2GqgURYiKQw81M1Uy1mLqauRIxgVhEmFkkxVilKsVIwgBM1d3UnImEBQwYPkXCMQQWEWJiAkDMRCTMLMLMwgRAza1o1qK5ZNWcrRSNqdnd2fnKm29++9vf+Tt/9A28cPj2HQhADjgAx7/B/DmAiJiJyOEOMwx9bpfry4uLg4Pjo+Pjk9PT+WLR972ZhyAxxqqumdmslFxyyWZKABEY19yZSUKoYqrqqkpRgoQgMYS4kVKMIQQJIUqQrhvOzi+Wq3VWBcASATbTEFLdjGJMTByCxJBKHuaLq7ZtSymBaTxqVIf5fHZ6fPzok1/NL2csGE2am/s7MUrXrlU1hdQP+Xy2qMeTL3z+i5/57Oc+88pnmtF4vWqLapAAsLmZeVF1gIiCSJBIQnAnkapKo1GztbU1noxHozrGKEL3Hhzj2uEPbgEoxYlx/+EJrh2+fRNWSt/16/VyfnV5cb68vGhXS89DFCI8x4AEEWLAAC+qgDsAes7MhlJyzv3Qk4Tx1lZdN8QSYogSWUjATGAimJva4urq6PDo8ePHv/7Vr69mVxKlCilWqamrqq5B1LYtgKZpbt688eprr710+87O9nZMQVWLq5s63AED4AZ1Vc05Fy2lqGoBSERCSBxDDKmqawmJWcBCHJxYHWbIquYASYxVszEa1+NRqipOgYVBuPfgGC8cvnMb5thgwoZ5267Oz87ee++9P/2nf/zBBx/MZldD13HgGEJVpZSqKqYQhFlMy2q97ocu56Il56IAUkypSlVVxxhFhJjNLOfc913X9d267Yd+Q9WCCDERyOFmpmqmWkxdzVyJmEAsIswskmKsUpViJGEApupuas5EwgIGDJ8i4RgCiwgxMQEgZiISZhZhZmECoOZWNGvRXLJqzlaKxtTs7ux85c03v/3t73z162/uv7TfjEZ3v3eEFw5/cAuA499g/hxARMxE5HCHGYY+t8v15cXFwcHx0fHxyenpfLHo+97MQ5AYY1XXzGxWSi65ZDMlgAiMa+7MJCFUMVV1VaUoQUKQGELcSCnGEIKEECVI1w1n5xfL1TqrAmCJAJtpCKluRjEmJg5BYkglD/PFVdu2pZTANB41qsN8Pjs9Pn70ya/mlzMWjCbNzf2dGKVr16qaQuqHfD5b1OPJFz7/xc989nOfeeUzzWi8XrVFNUgA2NzMvKg6QERBJEgkIbiTSFWl0ajZ2toaT8ajUf25//gC135y+5t3/+AJrlHOmRhMG4Dj7oNnuPbrf8LrxWJ2ebaYzdrFvAx9YMBMzdhdmDYABxyfYiYmEJvpcK3texYZb01TXUMksMQgIYiwRAlJIgFutri6Ojw4ePTJo48//vj07NxyBsBBqlRVTS3MwzCIyGg8uX3n9he++IWXX355b3+vqmqHFdWspWhWU1N1c1MDXFVzyXkopWQ1BJEQY6yaqkqxalgCiEECEhABbI7f/L27uPYv/ttFUzd1M6pGTawjBWamew+f4drhO7dBBHeYY4MJBLivVqvzk5P3333/T/7kj3/64Yfz+XwYhiAcQ4ifkhBjZOKsuW1XfT8MQzbVYs5MIcYqpaquU0oxRiZS1a7v2nXXtut12/Ztl7W4GTEzEYEAqJubqZai6ubuRsQEYhFhZuEUU13XUQIAcy3FTNXhzBxCEBa8QMIxhiBCYGICQAQQBxEWYWYRwYabmpeci6oWzcVy1hib6c7OV954/W9/+zt/94++gRcO374DAcgBd/zbzDewQcRMRA53mCHn0q/by8vZ8eHx0fHxwdGzq6tZ3w/mHkKoUqrqWphV8zDkfuhVC+BMxEQAzIyZ66qK11KV6rpqNuoqVXVKIYQYggDIRddtO99Yrtt2rbkYKEhIqRqPx9PpXkxJTQFElr4fLmcXXdu6W2BOVWB47rv5YnZ6fHhy+uzy4oyg+/u7TdMwFA4zdP0wX7bT3b3X3/jqZz/7udt37khM86vFMAwxJBYBYGq5FDOwEDEHEQBqJkGapp5MJlvT6WQ8quoqBBGhew+Oce3wB7cAlOLEuP/wBNcO374JLzoMuW3Xi8Xs8mwxm7WLeRn6wICZmrG7MG0ADjg+xUxMIDbT4Vrb9ywy3pqmuoZIYIlBQhBhiRKSRALcbHF1dXhw8OiTRx9//PHp2bnlDICDVKmqmlqYh2EQkdF4cvvO7S988Qsvv/zy3v5eVdUOK6pZS9Gspqbq5qYGuKrmkvNQSslqCCIhxlg1VZVi1bAEEIMEJCAC2BzqMAiLxFg1dVM3o2rUxDpSYGYC6N7DY7xw+M5tmGODCQS4r1ar85OT9999/0/+5I9/+uGH8/l8GIYgHEOIn5IQY2TirLltV30/DEM21WLOTCHGKqWqrlNKMUYmUtWu79p117brddv2bZe1uBkxMxGBAKibm6mWourm7kbEBGIRYWbhFFNd11ECAHMtxUzV4cwcQhAWvEDCMYYgQmBiAkAEEAcRFmFmEcGGm5qXnIuqFs3FctYYm+nOzlfeeP1vf/s7X/vaV3Zv3RiPRne/d4QXDn9wy/FvM9/ABhEzETncYYacS79uLy9nx4fHR8fHB0fPrq5mfT+YewihSqmqa2FWzcOQ+6FXLYAzERMBMDNmrqsqXktVquuq2airVNUphRBiCAIgF1237XxjuW7bteZioCAhpWo8Hk+nezElNQUQWfp+uJxddG3rboE5VYHhue/mi9np8eHJ6bPLizOC7u/vNk3DUDjM0PXDfNlOd/def+Orn/3s527fuSMxza8WwzDEkFgEgKnlUszAQsQcRAComQRpmnoymWxNp5PxqKqrz/5HZ7j2k9vfvPsHT3CNihYATCBs+N0HJ7j2+I/ierW8ujxfzC7X80XpW8Cg7lbMDGbm5qbm5u7ELCIcRJgBFLNcSs6ZhavRmFMFQFiCSIwSJcQQ65gCCwGr1fLk+NnTpwePPvnk+Oh4Nrvs1q26hRDrpk4hgVFV1XQ6vX33zhe++IV7L9/f39+rm8YBM82qQxmGvs05F1WYA3C3kksuuR+yqTNL2qibqqolJmYBCMQgAYkRgfjf/b27uPYv/2FXV3XVNHXTSBVYGIx7D49x7fD7d7Dh7mYAiBkEuC/mi5Pj4/ffe/eHf/bnv/j5zxbzRc4DE0IQFokSREIQZhFTbbv1MAw5F1MDQCIskmKs6jqlFGMEoKpd161Xq/V6tVq1OQ+q6u60wcxEAMzdVEtRUzVXAERMTEwizCwcJdajJooUVc0l52xuRCQhNHWVUmIWEDYIHGMggjvcHNdYiFiCMIsEERDDTc215KJmqllhihjq6fb09Tdef+utv/V3/ugbeOHw7TsQgHwDcCP8DQaZw90B2mAicsDhhlJ06IbZ1dXpyenB4fGTg6eXF5dt18NdQkgp1VXNwlpyP/QbpWS4M4FFiAhACKFKKYbIwlVdb00n4/F4VDcSAwAhjimY+zAM67adXy3ni/liuej73hRVXe/s7O7u7u3t7nFIXduqagyhlLJcLHMZqphAVnLPsBQjPLfr1fHRwc9//uHs/LxKElKIQdx0veohMt7auXf/M19+/Y2X778y3d5x99nl1TAMKSYJSUTgGLKqq5AQA0RwL6ohhtFoPJ1OptvTyXgUU5TATHTvwTGuHXz/JQDmBOD+w2e4dvj2Tbh6ydp169Xy6vJ8MbtczxelbwGDulsxM5iZm5uam7sTs4hwEGEGUMxyKTlnFq5GY04VAGEJIjFKlBBDrGMKLASsVsuT42dPnx48+uST46Pj2eyyW7fqFkKsmzqFBEZVVdPp9PbdO1/44hfuvXx/f3+vbhoHzDSrDmUY+jbnXFRhDsDdSi655H7Ips4saaNuqqqWmJgFIBCDBCRGBGJAwMISUqrrqq6apm4aqQILgwH4vYfP8MLhO7fdDAAxgwD3xXxxcnz8/nvv/vDP/vwXP//ZYr7IeWBCCMIiUYJICMIsYqpttx6GIediagBIhEVSjFVdp5RijABUteu69Wq1Xq9WqzbnQVXdnTaYmQiAuZtqKWqq5gqAiImJSYSZhaPEetREkaKqueSczY2IJISmrlJKzALCBoFjDERwh5vjGgsRSxBmkSACYripuZZc1Ew1K0wRQz3dnr7+xutvvfW3vvr1N/df2m9Go7vfO8ILB9+/BbgR/gaDzOHuAG0wETngcEMpOnTD7Orq9OT04PD4ycHTy4vLtuvhLiGklOqqZmEtuR/6jVIy3JnAIkQEIIRQpRRDZOGqrremk/F4PKobiQGAEMcUzH0YhnXbzq+W88V8sVz0fW+Kqq53dnZ3d/f2dvc4pK5tVTWGUEpZLpa5DFVMICu5Z1iKEZ7b9er46ODnP/9wdn5eJQkpxCBuul71EBlv7dy7/5kvv/7Gy/dfmW7vuPvs8moYhhSThCQicAxZ1VVIiAEiuBfVEMNoNJ5OJ9Pt6WQ8iim++h+e4tpPbn/z7h88cXcAZKaAEwEOuN99eIJrT/6Xulst57OLxWy2urrq2xVcoepmpRQtRcuQ81BU4Q7msBGDhBBYwOSAmlEQSYlCcAMLR5EYgkhMQaqQooiw9H13NZudn54dHx4dHx8fHR/NLi/bVWvAaNSMmqYaNZPJZLq9fev2rVdeffXO7VvT7Z26rp3gbsV0GIaua/uh16LmxsQAimreGEpRZZIYQ1U3MSawgBkQZgJFZgELWP69v3cX1/6f/6GvqqaqmnpUSwosBMG9B8e4dvj9OwBcTU0BCAsJwX1+dXX49Ol7777343/2F7/8xS+Xq8WQh8DCDCIRYQFzkA0zG4Zu6Ieiaq4wgJlFYggpRokxiAAoqn3Xt+163bZ92w05u5ubExMRExMTmbuplqKmaq4AiJiYggiBiSmGUNc1s2guOeehZDdjkRTTeDyqqopFhBgAMQURAEXVTHGNWYg5iLAIMwuTOdzdiqoWNTclB4eq3ppMXn/9N956663/8p98HS8cvn0HApC7m+Ea4VMMMofDASIQE5Fjwx1arOSymC/Pz84Oj44ePT44Pz9bt52pskiMadTUTJw1D/3Q9V0pGe4gCkGIyB0iHEKMITBzPWp2d3a2ppOmaZjFTIkoxgiQltJ27ezy6mo+m11drVfrnEtVNzdv3ryxf3NnZ09CWCwWWqwZNXBv21aLMgMwK4MIj5uUYmDy87Nnv/z5z06eHZXcw1SimGnXD6lq9m7cvn3n5dc+9/mbN29WdZNV5/N5ySXGFCTFlECk5u4gwoY73LSohhgmk/F0Ot3d3W7GTQyBmJjo3oNjXDv4/kt4jgG/9+AZrh2+fRNQL8WGoVst57OLxWy2urrq2xVcoepmpRQtRcuQ81BU4Q7msBGDhBBYwOSAmlEQSYlCcAMLR5EYgkhMQaqQooiw9H13NZudn54dHx4dHx8fHR/NLi/bVWvAaNSMmqYaNZPJZLq9fev2rVdeffXO7VvT7Z26rp3gbsV0GIaua/uh16LmxsQAimreGEpRZZIYQ1U3MSawgBkQZgJFZgELWEBCEiTElKqqaqqqqUe1pMBCEJj7/YfP8MLB27fUFICwkBDc51dXh0+fvvfuez/+Z3/xy1/8crlaDHkILMwgEhEWMAfZMLNh6IZ+KKrmCgOYWSSGkGKUGIMIgKLad33brtdt27fdkLO7uTkxETExMZG5m2opaqrmCoCIiSmIEJiYYgh1XTOL5pJzHkp2MxZJMY3Ho6qqWESIARBTEAFQVM0U15iFmIMIizCzMJnD3a2oalFzU3JwqOqtyeT113/jrbfeeuOrX9l/ab9pRne/d4QXDr7/kuEa4VMMMofDASIQE5Fjwx1arOSymC/Pz84Oj44ePT44Pz9bt52pskiMadTUTJw1D/3Q9V0pGe4gCkGIyB0iHEKMITBzPWp2d3a2ppOmaZjFTIkoxgiQltJ27ezy6mo+m11drVfrnEtVNzdv3ryxf3NnZ09CWCwWWqwZNXBv21aLMgMwK4MIj5uUYmDy87Nnv/z5z06eHZXcw1SimGnXD6lq9m7cvn3n5dc+9/mbN29WdZNV5/N5ySXGFCTFlECk5u4gwoY73LSohhgmk/F0Ot3d3W7GTQzhle+d4NpPbn/z7h88cXcA5G6AAw53OO4+PMG1g/913C2Xy/lsfnmxnF2267XnwazAXXMe+m6j7dqcBzNjZgkhpFilFNNzIcYggQIriwPOYJIUhEUCSyRhpsgiIcIsD0O7bheL+fnJ6eMnT06Oj8/Pz4ecR6Px1nRrb39vd2d3a2d7b2/vxs2XpttbTdNEiWAoYKa55Lbrcz9ky3CwMIPU3bIOZVBVOEhCFRNLNIcRMQtLJAkikSSwxH//v7mLa//yv+9T3TRVnUZNrAIzQXDvwTGuHX7/DgBV85IBUIjCBLOr2eWjR4/ef/e9H//Fjz76+KP1ap3LEFiYQcQACSASJAZ3z32fdSiqppqLA2CmIBJiJCIA7l5yHnIZ8pCHXHIuWkzNYcxCxCIMwNzdLOdi+hwAFiYmJmEGgUUkVUmIi6rmkvPggISQUpqMx1VVhSDMAoCYYxB35JyLKgAmEJiFiCUIswgRA3A3V1c1dQOYKKaqGU8mv/HlL/32W7/9n//jN/HC4Tt3QHC4uTquET5FRAC5Y4OIGESOT7m6qq9X6/Pzi2fHz548fnpydrparvqcQQghjpqaSYqWnIe267QUAMzEIgBKUbiDiZmjhNHWeH9/fzqdNHUjIqVkIo4pxbAR27Y9PTu9vDi/nM3mi0W76mKM+zdu7O7t70x3QLxYLAmYbu9UMQ55GLpuuVoyfDSqmjqlKjZJqpQ0d7PZbD47n89nXbcyzUwUYxWrOtaT0WR7d/fGaDyhIG42DIOaBxaWFGMKQcDCtMFqumGqRTXEMN2aTne29nZ3RuOGhZkIwL0Hx7h28P2XzF04AH73d45x7fDtG4BDi5fSLZfL+Wx+ebGcXbbrtefBrMBdcx76bqPt2pwHM2NmCSGkWKUU03MhxiCBAiuLA85gkhSERQJLJGGmyCIhwiwPQ7tuF4v5+cnp4ydPTo6Pz8/Ph5xHo/HWdGtvf293Z3drZ3tvb+/GzZem21tN00SJYChgprnktutzP2TLcLAwg9Tdsg5lUFU4SEIVE0s0hxExC0skCSKRJLBEkIgECTGmKtVNU9Vp1MQqMBME5n7/4TO88OTtW14yAApRmGB2Nbt89OjR++++9+O/+NFHH3+0Xq1zGQILM4gYIAFEgsTg7rnvsw5F1VRzcQDMFERCjEQEwN1LzkMuQx7ykEvORYupOYxZiFiEAZi7m+VcTJ8DwMLExCTMILCIpCoJcVHVXHIeHJAQUkqT8biqqhCEWQAQcwzijpxzUQXABAKzELEEYRYhYgDu5uqqpm4AE8VUNePJ5De+/KXffuu3X//KG3s395pxc/fhEV548s5NxzXCp4gIIHdsEBGDyPEpV1f19Wp9fn7x7PjZk8dPT85OV8tVnzMIIcRRUzNJ0ZLz0HadlgKAmVgEQCkKdzAxc5Qw2hrv7+9Pp5OmbkSklEzEMaUYNmLbtqdnp5cX55ez2XyxaFddjHH/xo3dvf2d6Q6IF4slAdPtnSrGIQ9D1y1XS4aPRlVTp1TFJkmVkuZuNpvNZ+fz+azrVqaZiWKsYlXHejKabO/u3hiNJxTEzYZhUPPAwpJiTCEIWJg2WE03TLWohhimW9Ppztbe7s5o3LDw/YfPcO0nt7955/cfAzB3cjfAAYc73O8+PMW1w/9ta1ivFrPZ/PJifnnRLpcld56zm+Y8tG3btW3XrXMezAzEHEJMsUqpquu6aVKqqyaRhOzmABGHIDGmKEIbgKsLICxCIkJQL5rns6vDw8OnTw8Onj5Zr9d10+zu7d29e2f/xo2t7e2tra3RZFJXiUSEhRgOGJBVh77rh2KqAFhEGBtFdRiyaVE1gEUEoKzuEIkSQgohcUohJJL0W3//gtlYcAAAIABJREFULq793/+wq+qmrptqVMcqMhME9x4c49rh9+8AUDUrGQCHKEwwm80uH33y6N13//Kf/+jHH330cbtel6IxMBHhGgFBWEJ0WOlz1qGoatYhD+oIIkRMTG5qqlnNVNXM1dzM3NRMtQBgFiIWYQIcUNWci+lzAFiYmJiEGRvCEkMkkMF0IxcAHEIKsR41MYYgQmBiYpEYIxFpLuqGa0xgFhIJzCxMRCDA4ermVswILJJiqsfjyZe//KXf+ta3/ov/6at44fCd2yByuLk6/oYDtAEiOAACgUHk+GsON3RtN7u8evbs5OmTg2cnp/P5vOs6g7GEUVOziKnmnPu+z6UAIKIgApC5mm8YMYcQRqPRzs7OZDJpRk2UgOc4BGGmEKTv+8vLy4uLi8vZ5dVsdjmbE9HO7t7uzu50ukMsq+UyhrC/fyOlKg9D17fr5VIE0+l0VKcYJUaqY2SCuw7tanZ10a9XBmXmVFUhVJDEnFgCWIhZWEiEiOAsTBJSTKmqahEBoGYll5yHohpimG5Pt3eme3u7o6YRoQ13v/fgGNeevPMS4MIB8Lu/c4xrh2/fABymUB3Wq8VsNr+8mF9etMtlyZ3n7KY5D23bdm3bdeucBzMDMYcQU6xSquq6bpqU6qpJJCG7OUDEIUiMKYrQBuDqAgiLkIgQ1Ivm+ezq8PDw6dODg6dP1ut13TS7e3t3797Zv3Fja3t7a2trNJnUVSIRYSGGAwZk1aHv+qGYKgAWEcZGUR2GbFpUDWARASirO0SihJBCSJxSCIkkMQmLUEhVSlXd1HVTjepYRWaCwNzvP3yGF568fctKBsAhChPMZrPLR588evfdv/znP/rxRx993K7XpWgMTES4RkAQlhAdVvqcdSiqmnXIgzqCCBETk5uaalYzVTVzNTczNzVTLQCYhYhFmAAHVDXnYvocABYmJiZhxoawxBAJZDDdyAUAh5BCrEdNjCGIEJiYWCTGSESai7rhGhOYhUQCMwsTEQhwuLq5FTMCi6SY6vF48uUvf+m3vvWtN958Y+/mXtOM7n7vCC88eeem4284QBsgggMgEBhEjr/mcEPXdrPLq2fPTp4+OXh2cjqfz7uuMxhLGDU1i5hqzrnv+1wKACIKIgCZq/mGEXMIYTQa7ezsTCaTZtRECXiOQxBmCkH6vr+8vLy4uLicXV7NZpezORHt7O7t7uxOpzvEslouYwj7+zdSqvIwdH27Xi5FMJ1OR3WKUWKkOkYmuOvQrmZXF/16ZVBmTlUVQgVJzIklgIWYhYVEiAjOwiQhxZSqqhYRAGpWcsl5KKohhun2dHtnure3O2oaEbr34BjXfnL7m3d+/4m5A05uBjjI4Q73uw9Pce3wf58O6/Vydjm/vLi6OF8vlmVoc9+V3A9937Xt0Ld936tmA5iFREIIEmOVqqZpUtOkupbA2QxMHEKdUl01MUQiaNah73woAGIIVapTDEG474ery6vDp0//6qO/ml3NUqr2btx49dVXXnrp1mR72jQNCQHspiAiEWY2wEyHPGhWdQMQRJgZBFPLeci55JyLOhxqpupOIinFlKqqiamKsSYJv/UPXsa1f/HfrZtmVDdN1TSxihIJhHsPjnHt8Ad34HA1NQUgLCQE96vZ7PGjx+/95f/7o3/2Fx999HHfduYWA28AcDMADJYY3D3nPuehqA790HW9qXIMTGTqRUvf9aq5mBMoBiEigBxuWgAwhIRFGCCHq2rOxbSoGgBiMAkJEwiAMMtzjA13VcMGkTCHGIUY14gpxlClWmLA/w8TmIWEhYklEBEIcMDdzNQBY2YJMY1Hky99+Uvf+ta3fvcPv4EXDt++BSEQ3M3w19wNABEzkeH/YwreejS9rjux/9dae+/ned5TVfWxuprdEinxJFF2LnQxgEAfZBkDJDAJBQkwnyAYBAgGQb7BIEhgT/IFRndBEhiTQQT2xczYBuXceJABZImiJEsmaR66WV2nrqr3rff47L3XWnm7pJ7J70cAGASAHL/lgGHT5/nV/Oz0/Onh0enp2eXl5XK1UlUS7rpWgrha0dr3fa0VABOJSIjCzAAZnAGREFJsm6bpBqPhsGu7pmuEuDyXS8ml1Fzyark8vzg/f/bs9PRZzmUyGU8mu3t7N0JMfd83TXPn1h2JYbVYlFoDIaYw6NomBWYmUlcVGAu7ls1mCbfYJGGqVQ0cY1MUy1VftAYOqe3G43GMsZQCQDg2TTMYjWJKAEx10/el5FI1JplMdvb2dm7e3Gu7jokAB3D/nWNce/LeHcCFAuAH7xzj2tMf3gIcblDNq9Vienl1eTG7OF/NFzWvS7+ppc99v1mvc7/u+161GMAsJBJCkBib1HRdl7outa0ELmZg4hDalNqmiyESQYvmfuO5AoghNKlNMQThvs+zy9nTL7/86OOPprNpSs2NW7e++tWHd+7cHe1Muq4jIYDdFEQkwswGmGkuWYuqG4AgwswgmFopuZRaSqnqcKiZqjuJpBRTapoupibGliSAhCWKSEhN1w3armu6LjZRIoGgag+/f4oXDn94V00BCAsJwX02nT7+4vHPfvqT/+dHf/3xx5/06425xcBbANwMAIMlBncvpS8lV9Xc582mN1WOgYlMvWrtN71qqeYEikGICCCHm1YADCFhEQbI4apaSjWtqgaAGExCwgQCIMzyHGPLXdWwRSTMIUYhxjViijE0qZUY8P/DBGYhYWFiCUQEAhxwNzN1wJhZQkzDweiNN9/44z/+47d+560bt28NR4OD7x/jhcNHdwy/5W4AiJiJDASAQQDI8VsOGDZ9nl/Nz07Pnx4enZ6eXV5eLlcrVSXhrmsliKsVrX3f11oBMJGIhCjMDJDBGRAJIcW2aZpuMBoOu7ZrukaIy3O5lFxKzSWvlsvzi/PzZ89OT5/lXCaT8WSyu7d3I8TU933TNHdu3ZEYVotFqTUQYgqDrm1SYGYidVWBsbBr2WyWcItNEqZa1cAxNkWxXPVFa+CQ2m48HscYSykAhGPTNIPRKKYEwFQ3fV9KLlVjkslkZ29v5+bNvbbrmOj+O0e49uP9b9/7l1+aG+DkZoCDHO5wP3j3DNcO/9WkbNaL2WwxnU7Pz1bz2Wa9qutVyZt+ven7dcmbUnpVJwZYWAQSmCQ2qeu61HVt11EIBiPhGGOKTde2MQTAtWpeZ8vZTZmkSamNKabkVa/mVyfHJ5/+wyfTq6sU096NvftfeXD71u3ReBJSAhngZjCAmYiZSAymqqZq5g4jYgBMMLdatZRa+lxqLqUWNVMHi8QYU5uarmm6mBoO8R//T1/Ftb/5s1XXDdqua7ouNpGEwHjw7jGuHT7aJ5C7aVUAwkJCcJ/Npo8/f/yzn/7kr99//+OPP8k5w0yEt4gIDphji8lNSyk5Z1PNOa82azNLMTKJwaFuUDzH6qYli4TReAzDfH6V8wYsQszCYDIz3Sq1urmpO4hAzEzCTGYqLFEiC7sbDIQtMjiBWBj/EVOQkJoUQowxEAsAAphALCxCzCJMRNhyuLnDzAFjB6UYh6PRa6+/8Uff/e4//T/+EV54+t5dEIHg7tgimMPdABARiAACwCAA5PhPHCWX5WJ9cX5xeHh8cno2vZzOl8tSMkBNE5nY3EotJfdVlYmYWYKEEGKKBHI4AGIOLBRC27SDwWA0GLZdJyIl503OuV9XVQI2m/XZs7Pjp8dPDr9cLZYxpcFguDPZaZqWibrBYG/vZkxxs16L8GQyHg26rm1CEJhXK7Xv4RpTYHKthYibNhCwydUNIaZc9Opq0ZcaQmi7we7OTgyxqAIQlqZpBuNRSi2AWvJms+lzNvemSbu745293b3dvcGgJYI7zP3Bu8e4dvjorrkLCeAH7xzj2tMf3gIcZq5aNuvFbLaYTqfnZ6v5bLNe1fWq5E2/3vT9uuRNKb2qEwMsLAIJTBKb1HVd6rq26ygEg5FwjDHFpmvbGALgWjWvs+XspkzSpNTGFFPyqlfzq5Pjk0//4ZPp1VWKae/G3v2vPLh96/ZoPAkpgQxwMxjATMRMJAZTVVM1c4cRMQAmmFutWkotfS41l1KLmqmDRWKMqU1N1zRdTA2HyJxIJIYUmqbrBm3XNV0Xm0hCYLjpw++f4YXD9+5qVQDCQkJwn82mjz9//LOf/uSv33//448/yTnDTIS3iAgOmGOLyU1LKTlnU805rzZrM0sxMonBoW5QPMfqpiWLhNF4DMN8fpXzBixCzMJgMjPdKrW6uak7iEDMTMJMZiosUSILuxsMhC0yOIFYGP8RU5CQmhRCjDEQCwACmEAsLELMIkxE2HK4ucPMAWMHpRiHo9Frr7/xR9/97lvf+tat/dvD0fDBf3WGFw4f3cUWwRzuBoCIQAQQAAYBIMd/4ii5LBfri/OLw8Pjk9Oz6eV0vlyWkgFqmsjE5lZqKbmvqkzEzBIkhBBTJJDDARBzYKEQ2qYdDAajwbDtOhEpOW9yzv26qhKw2azPnp0dPz1+cvjlarGMKQ0Gw53JTtO0TNQNBnt7N2OKm/VahCeT8WjQdW0TgsC8Wql9D9eYApNrLUTctIGATa5uCDHloldXi77UEELbDXZ3dmKIRRWAsDRNMxiPUmoB1JI3m02fs7k3TdrdHe/s7e7t7g0GLREO/uQI1368/+17//JLwM2d3BRwPOdQP/j+Ga49+fNx6Teb5XIxm15dnC9ml8urWd6sPG9y3vSbdek3pWa4kwiYweIkYIlbbdd0XTsYhBTBwkFiDCnGKIGZCYCZq3tRq9lU2YlJmih9zleXs/Pzi/OLZ+s+xxTHo/HNOzcnOztt06WmiSlKFBJxwMxALEGIBIC5qdaqqkUNSg4iA9hUc86bftOvN6VWdQIxSQihiWmrS20XU/Nf/ItXce0//ItN23VN26VBG1KAMOAPv3+Ca08e3WUQ3FUVDmEhIcBnl9PPP//sg7/9yY/ef//TTz6tqq4GGIAoTCwEuGmpqmWrVi1aai4l50yErm1CiETStmkwHA4GbQpptV4/PToMIb7yyisO/MOnn07PLxwgZhZ2eClqWh3uZgaYA1AiDhwAVNMAbtuWQLlmmEWJYAbc8RxtMRPIATAJkcTYtm2QiC0Gk4gQs5AEAYHh5gZzx28Z3BBjGk8mX3v11d//vbf/2f/1B3jh6aN9EAFwd2wRtswBuOM5ImIQthxwB0BE2HLUqv2qv5zOnj49OT19dnE5XcwXfe7djJgcXmvRmvuc4UYsQSSmsCUxMJGp4TkSkZBik9q2adq2jSkFEQDuMFM3AzBfzE9PT7788sknn3x2fv5MayWSpk1dOxhPJqOt4aBt2pjiZDK+c+fOjd3d4WgYREouufSb9RrQJiUWAczN1dRd4QAEoFp9vdmouQRJKbVtF2MAIMwSQkypadsYEoCc83K1UFUW6bpud29nd3eys7PTtg2umePBu0e4dvhoH3ACAX7wzgmuPf3hLXeHqqmWfrNZLhez6dXF+WJ2ubya5c3K8ybnTb9Zl35TaoY7iYAZLE4ClrjVdk3XtYNBSBEsHCTGkGKMEpiZAJi5uhe1mk2VnZikidLnfHU5Oz+/OL94tu5zTHE8Gt+8c3Oys9M2XWqamKJEIREHzAzEEoRIAJibaq2qWtSg5CAygE0157zpN/16U2pVJxCThBCamLa61HYxNRJSiG1MqW26tuuatkuDNqQAYcABf/j9U7xw+N5dVYVDWEgI8Nnl9PPPP/vgb3/yo/ff//STT6uqqwEGIAoTCwFuWqpq2apVi5aaS8k5E6FrmxAikbRtGgyHg0GbQlqt10+PDkOIr7zyigP/8Omn0/MLB4iZhR1eippWh7uZAeYAlIgDBwDVNIDbtiVQrhlmUSKYAXc8R1vMBHIATEIkMbZtGyRii8EkIsQsJEFAYLi5wdzxWwY3xJjGk8nXXn3193/v7W988607B3d2Jjtf/SeXeOHw0T62CFvmANzxHBExCFsOuAMgImw5atV+1V9OZ0+fnpyePru4nC7miz73bkZMDq+1aM19znAjliASU9iSGJjI1PAciUhIsUlt2zRt28aUgggAd5ipmwGYL+anpydffvnkk08+Oz9/prUSSdOmrh2MJ5PR1nDQNm1McTIZ37lz58bu7nA0DCIll1z6zXoNaJMSiwDm5mrqrnAAAlCtvt5s1FyCpJTatosxABBmCSGm1LRtDAlAznm5Wqgqi3Rdt7u3s7s72dnZadsGwP13jnDtx/vfPvjBobsDTm4Vv+GA+cH3T3Ht8Z9Pat/n9Xo5n80vL66mF6ury/VyYf26bNZ9Xpd+Y6qAsQhEDAJmJZaQmrZrBoN2OEhtF1IIMYYoUQROBMCc3NigVTXnmquWAvfAUktezpfTq6vp9GKTs0gYDIc3bu4Nx+OuHcQmxSaFFGJKYHYDCCQhiBAzAFOttfQ5W61uChgRm2qppc+5X2+KVjWAtoJIjKmJqUtNF9rmnf/1DVz7D/9i03Zd03axa2KKEALRw+8f49rjR3cFBHdVAyDEJAT47PLy888//+Bvf/Kj99//9JNPTU2fKwSISBAmwNRKyaWqm5pqraq11lKJqUmxSU3bNqOdyZ1bt0aTkXCaTS8+/uRjCfGbb70Fwq9++cvj01MtavAQxcxzzlUrADcY3LagBE4xAdBSJMiwGxK4aoYhhNBcc2C5WgnzZDKJMVXXvs+bzdrdY2qCCH6DhUlCECIGY8sNDgNAAIEBmFmK7Xgy+dqrX//Od77zP/zff4QXnj66B8KWu2OLCNfMnwNAREyELXe4w0BMIIKjqpZNmU3nh0cnZ2fnFxez+Xyx2axVC8hNtZScSy4lw41FQpAYg4QQRIjJzcyxJSIxpK2mbVJMWzEmEQkiICK4A4vF4uTk6IvPP//1r399dHS8XK5UVUSath2PJoNBEyQOx6Mbe7v7+/tfefiVO7duj8YjFs5bfV6v14C2qQkpMsFM+5zdjJjdCcZVtRZ1IASRKCkmFhEmYo4hxBhDSiJCRDnnxXJeq6aUhqPB7u7OZGeylZqGCVvmePDuEa4dPtoHQHAAB++c4NrTH952d1N11dr3eb1ezmfzy4ur6cXq6nK9XFi/Lpt1n9el35gqYCwCEYOAWYklpKbtmsGgHQ5S24UUQowhShSBEwEwJzc2aFXNueaqpcA9sNSSl/Pl9OpqOr3Y5CwSBsPhjZt7w/G4awexSbFJIYWYEpjdAAJJCCLEDMBUay19zlarmwJGxKZaaulz7tebolUNoK0gEmNqYupS04W2ibGNqYmpbZuu7bqm7WLXxBQhBCKwP3z3BC8cvndX1QAIMQkBPru8/Pzzzz/425/86P33P/3kU1PT5woBIhKECTC1UnKp6qamWqtqrbVUYmpSbFLTts1oZ3Ln1q3RZCScZtOLjz/5WEL85ltvgfCrX/7y+PRUixo8RDHznHPVCsANBrctKIFTTAC0FAky7IYErpphCCE01xxYrlbCPJlMYkzVte/zZrN295iaIILfYGGSEISIwdhyg8MAEEBgAGaWYjueTL726te/853vfOOb3zi4f2+8u/vKP5nihcNH+9giwjXz5wAQERNhyx3uMBATiOCoqmVTZtP54dHJ2dn5xcVsPl9sNmvVAnJTLSXnkkvJcGORECTGICEEEWJyM3NsiUgMaatpmxTTVoxJRIIIiAjuwGKxODk5+uLzz3/9618fHR0vlytVFZGmbcejyWDQBInD8ejG3u7+/v5XHn7lzq3bo/GIhfNWn9frNaBtakKKTDDTPmc3I2Z3gnFVrUUdCEEkSoqJRYSJmGMIMcaQkogQUc55sZzXqiml4Wiwu7sz2ZlspaZhwv13jnDtx/vfPvjBobsDILeC5wgOGA6+f4JrT/58z0peb1br+dVierGYTRdXl5v5bLNclM2y5E3NvZoCxiEQiTMbiRFzbJpu0A2G3WjUDrvBoAspxSDYMrOqWtWqwtxLKX3OfV822WrFljs55vP506Ojq/mVAcPh8M7dO7t7e4PRqGkaFpEUQooSUxAhEmyxhBCE2dxM66bPJW9qyabVHea6Zaql1lLVtpxEhEOKMcWmTamT1PzJ//IGrv2/f7Zq2q5pu6ZtQoqUBEIP3z3GtceP7gqRGVwVgIgQAe6z2fSLzz//8Kcf/PX773/yyac1l6qlluyOKEIEAtyhpRqMAQLcoaZuCncm6tpusju5ffv2wcHBYDgouZ6dnX308Ucs8uY33mSWjz766PjkZL1aV9MYxMw3ua+1uruamno1c1cibppGiFU1ShgOBiEKg2mLeTIZ37//krk9+eKJBHn11VcnOzvLzXp6eXl8fDyfL3HN8RtMTERCDBA74G7YIjCxEBEYjpTSaDT62quvvf322//9v/5DvPD00T2H4z8iwjXz5wAQERNhyx3uMBATiADUqqUvs+n86Pjs9Ozi4vxiPl+s16tSC+Cmtc99KbmUbKYsLMIhBBEJIsTkZgDMISIhhCY1TdNudV3bpCbGxBKFwIyt1Wp9dnby2Wef/fzDDx9/+eVyPi9FWUSYiYnw3GQyfvDSw69+7eU3Xnvt7v7+YDAMIqXkUspm0wNo2xRjYhF3s1rVjcDucIVuVQXAUWIIIUZmARkRC0tMIcQYQiDinPN8eWWqKaXBcLizuzOZjCeTcWoaJmyZ48G7R7h2+OgeAIICOHjnFNee/vAO4G5uRa3k9Wa1nl8tpheL2XRxdbmZzzbLRdksS97U3KspYBwCkTizkRgxx6bpBt1g2I1G7bAbDLqQUgyCLTOrqlWtKsy9lNLn3Pdlk61WbLmTYz6fPz06uppfGTAcDu/cvbO7tzcYjZqmYRFJIaQoMQURIsEWSwhBmM3NtG76XPKmlmxa3WGuW6Zaai1VbctJRDikGFNs2pQ6SU2IbYxNjG2z1XZN2zVtE1KkJBAC/OG7J3jhyXv7rgpARIgA99ls+sXnn3/40w/++v33P/nk05pL1VJLdkcUIQIB7tBSDcYAAe5QUzeFOxN1bTfZndy+ffvg4GAwHJRcz87OPvr4IxZ58xtvMstHH310fHKyXq2raQxi5pvc11rdXU1NvZq5KxE3TSPEqholDAeDEIXBtMU8mYzv33/J3J588USCvPrqq5OdneVmPb28PD4+ns+XuOb4DSYmIiEGiB1wN2wRmFiICAxHSmk0Gn3t1dfefvvtt976xr37B5PdnYf/9QVeOHy0jy0iXDN/DgARMRG23OEOAzGBCECtWvoym86Pjs9Ozy4uzi/m88V6vSq1AG5a+9yXkkvJZsrCIhxCEJEgQkxuBsAcIhJCaFLTNO1W17VNamJMLFEIzNhardZnZyefffbZzz/88PGXXy7n81KURYSZmAjPTSbjBy89/OrXXn7jtdfu7u8PBsMgUkoupWw2PYC2TTEmFnE3q1XdCOwOV+hWVQAcJYYQYmQWkBGxsMQUQowhBCLOOc+XV6aaUhoMhzu7O5PJeDIZp6Zhwv13jnDtx/vfPvjBoTu2yC0DhC0nAAfvnuDa4b++paX069VmOZ9PL1dX0+Vstry63Myneb0sZWMlV1fAmQTCCjZiIw6xSd2wHQ4Ho/Fg1G3FlFgIDmjVojUXUyV1VbWc+03O63XJxVXdnYDZ7OqLJ08up5dmPhyP7t+/d/PWrclkkrqOgkiITRMkpRASiwDMLDEIiTCRqpbc93mTN73WrFqqOUzV3GBVzdUBorAVY2pjbEJqQ2z+8z97Fdf+/Z8u27ZLbde0TWwSJyHhB+8e4drjR3eJCAY3hUNEiAD3q9n0yePHP/vggx/91fsff/xJzrnmvpbs6ixCTGxuMHcQIbAwMTPBHDBTddem7W7dunn37v7d/f0QZXo5Ozo+/uzzzyWGb775Zkrx8ZMvz87OZlezWqrE4OZ97nOpblb1uWrqbkTctE1gcTMJYdh2TYgUJAVJMd28dfPlV17Jpf7iF78A+Hd/51u37twxs4vLy48//uT84rxWNXeYObYYIGJsOcEdZg4QC5iFRQRMQBPTcDR69bXX3v69t//Zv/pDvPD00T13NzgTYYsI18yfA0BETIQtd7jDQEwgAlCrlr7MpvOj47PTs4uL84vZ7Gq1WtWcDaZaS+1LzrlkdyUiZmJmEQlBiMndAQKImWMIMaWu6QZb3aDp2iY1EiIDzCCiftNfXF58+eTJr3/1qyeHh7Pp5XK57HM2VXMIc4xh78bNl19++etf/9prr712586dtmmJqNRaSu77bKohhhhiCEIgczc3d4cTObttOQgSQ5AYo4DgbkQg5iASYpIgzFJKni8WZppSGgy73b2dyWQyGo/bJuGaOR68e4Rrh4/uASBUgA7eOcW1pz+8iy13N9NS+vVqs5zPp5erq+lyNlteXW7m07xelrKxkqsr4EwCYQUbsRGH2KRu2A6Hg9F4MOq2YkosBAe0atGai6mSuqpazv0m5/W65OKq7k7AbHb1xZMnl9NLMx+OR/fv37t569ZkMkldR0EkxKYJklIIiUUAZpYYhESYSFVL7vu8yZtea1Yt1Rymam6wqubqAFHYijG1MTYhtSE2HFIIKcY2ptS2XWq7pm1ikzgJCYP8wTvHeOHJe/tuCoeIEAHuV7Ppk8ePf/bBBz/6q/c//viTnHPNfS3Z1VmEmNjcYO4gQmBhYmaCOWCm6q5N2926dfPu3f27+/shyvRydnR8/Nnnn0sM33zzzZTi4ydfnp2dza5mtVSJwc373OdS3azqc9XU3Yi4aZvA4mYSwrDtmhApSAqSYrp56+bLr7ySS/3FL34B8O/+zrdu3bljZheXlx9//Mn5xXmtau4wc2wxQMTYcoI7zBwgFjALiwiYgCam4Wj06muvvf17b3/rW9+699LBaGfnwX95hhcOH+1jiwjXzJ8DQERMhC13uMNATCACUKuWvsym86Pjs9Ozi4vzi9nsarVa1ZwNplpL7UvOuWR3JSJmYmYRCUGIyd0BAoiZYwgxpa7pBlvdoOnaJjXK9zIUAAAgAElEQVQSIgPMIKJ+019cXnz55Mmvf/WrJ4eHs+nlcrnsczZVcwhzjGHvxs2XX37561//2muvvXbnzp22aYmo1FpK7vtsqiGGGGIIQiBzNzd3hxM5u205CBJDkBijgOBuRCDmIBJikiDMUkqeLxZmmlIaDLvdvZ3JZDIaj9smAbj/zhGu/Xj/2wc/OHTHFrkW/BYBOHj3BNee/vBuLaX26365WMymq9l0OZ8tZ7PN/GKzXmremBZ3hTuIFFCQOhkxh9R0o3Y0HIxHbdeFEDhIEAZgpWitVhRmwswGrVr7vFmvc99rLv2mX23W5+cXjx8/nk6nDkx2xi89fHjn7p3dvb3BcMgxxBRTk4JEjolFhIVDiCGyiDCZu9ZcSu4363xNNVc1uDngDjgRM0sMoY1NirEJseWQ/vH//FVc+/d/umzbLrVd07UxRW5EhO+/c4Rrjx/dJSI4XI0AZiZs2dVs9vTw8Kc//eD9v/irjz76+816k/teS3E4gZgBA+AE4iBRJMhzAAjupjmXtm1v37518+aNnd3dnPvPHz8+Ojo5Pn02HA6/+Y3Xx6PhxeX04uJiOp3mkiUGd/R9rlpNnytaa1VTZZGmbSKLm3OQLjVBAgGDQbe7t3fnzu2X7t+fL1Z/+5Of5Fxef+21Bw8f7N7YWyyWv/zlL05OzszNzNXMHVtu2HK4uZkBcGwJBREJUYjYOaU0GA5ff/313/+DP/jv/vz38MLhe/tm7nBiYiIjbDHI/DkARMRE2HKHOwzEBCIAtWrpy+xyfnh0cnZ6/uz8Yja7Wi4XJWeDqdZa+lJyKVlNibBFW8wx8G8Q8TVhkZRS13bDbjgYjQZdF1MKIRAxAwQrpS5Xi8uLi8OnT09PTs7Ozk5Oz06Oj9abPqU0GU9u3b55797BwwcvPXz48MHDh3u7uywC9z7nvs/r9brkDICZWESIARjgpkQSJAmRqkugEGMIKQQBoWp2w1YIEmLgEIWplLpcLcw0Nc1oONzZm2wNR8MmJSK4wxwP3j3CtcNH9wCQK4CDd09x7el7+9gyAF5Lqf26Xy4Ws+lqNl3OZ8vZbDO/2KyXmjemxV3hDiIFFKRORswhNd2oHQ0H41HbdSEEDhKEAVgpWqsVhZkws0Gr1j5v1uvc95pLv+lXm/X5+cXjx4+n06kDk53xSw8f3rl7Z3dvbzAccgwxxdSkIJFjYhFh4RBiiCwiTOauNZeS+806X1PNVQ1uDrgDTsTMEkNoY5NibEJsOSSRwCGKpJhS23ap7ZqujSlyIyIM4P47R3jhyaN9VyOAmQlbdjWbPT08/OlPP3j/L/7qo4/+frPe5L7XUhxOIGbAADiBOEgUCfIcAIK7ac6lbdvbt2/dvHljZ3c35/7zx4+Pjk6OT58Nh8NvfuP18Wh4cTm9uLiYTqe5ZInBHX2fq1bT54rWWtVUWaRpm8ji5hykS02QQMBg0O3u7d25c/ul+/fni9Xf/uQnOZfXX3vtwcMHuzf2FovlL3/5i5OTM3MzczVzx5YbthxubmYAHFtCQURCFCJ2TikNhsPXX3/99//gD37nd75196X7453J/XeP8cKT9+4ykRG2GGT+HAAiYiJsucMdBmICEYBatfRldjk/PDo5Oz1/dn4xm10tl4uSs8FUay19KbmUrKZE2KIt5hj4N4j4mrBISqlru2E3HIxGg66LKYUQiJgBgpVSl6vF5cXF4dOnpycnZ2dnJ6dnJ8dH602fUpqMJ7du37x37+Dhg5cePnz44OHDvd1dFoF7n3Pf5/V6XXIGwEwsIsQADHBTIgmShEjVJVCIMYQUgoBQNbthKwQJMXCIwlRKXa4WZpqaZjQc7uxNtoajYZMSEQ7+5AjXfrz/7YMfHLpji1wLfosAOnj3GNeevrevtVq/yavlYjZbXc1Wi9lqNlvPL/N6oTWbZlN1M4OrI5ureQVxSE037IbDwWiUujbEEIIIMxy1ZC1acyEgiRAYqrXPq+WyX6/71WaxWs5ms2fPzo+OjhbzORjjyWT//v3bt2/t3bgxHo/ToN1KXRdCALGEEGKKIco1YiGCm1Ytpd/a9LnPOdeaq5q7AxRYmGOIKcQUUxNTyyFJSN/7Hx/g2t/86apru9R1TdemJnIUFrr/zhGuPX50l4jYYWoAmJkAmM0XV8dHRx988MFf/ru//Pu/+9Viscy5d1O4ETEBrgZAhIUlxhDkNzgGcdO+zzGmm7du7OzsjMej9br/4snjs7Nni/V6NBy9/NWHXdNOp9PLq9lsOs2lSIwOK6VqrWqmqqVk3TIXpqZtmRnmQaRtWhF289FwsL9/7/ad27du3pzN5j/54KeL+erevf37Dx68/PJXi9Zf/d3fnZ6cVTc3NzN3qMHhcDNzVXNTBxwgZhaJMTCJOFJqBsPRG2++/t3v/uF/+3++jRcO39tXMwDExERG2GKQ+XMAiIiJsOUOdxiICUQAatXSl+nl1eHTk+Pjs/OLy6vpbDFf9KUHrGrVkkstpWQ1BTkAdwdAhCAcQhAJIUQRYZEYY9O0XdONRqO265qUWAITtgQwuNay3vSL+fxyenl2evrF48cff/TJfD4fjoa3b9/6yle+8tJLL929e3f/7v7tO7dHwxGAqpr7zXrTr1bLzabXWgBIiEEkBAFgZswSQxtYAJBIei6wCOC15qoKgFhiDCwizLXqerVUeNukwXC0szeeTMaj4TDGSAR3mOPBu0e4dvjoHgByBXDw7imuPX1vH0RwAK61Wr/Jq+ViNltdzVaL2Wo2W88v83qhNZtmU3Uzg6sjm6t5BXFITTfshsPBaJS6NsQQgggzHLVkLVpzISCJEBiqtc+r5bJfr/vVZrFazmazZ8/Oj46OFvM5GOPJZP/+/du3b+3duDEej9Og3UpdF0IAsYQQYoohyjViIYKbVi2l39r0uc8515qrmrsDFFiYY4gpxBRTE1PLIUlIYGEJIjHEpmu71HVN16YmchQWAuH+nxzhhcNH+6YGgJkJgNl8cXV8dPTBBx/85b/7y7//u18tFsucezeFGxET4GoARFhYYgxBfoNjEDft+xxjunnrxs7Ozng8Wq/7L548Pjt7tlivR8PRy1992DXtdDq9vJrNptNcisTosFKq1qpmqlpK1i1zYWralplhHkTaphVhNx8NB/v7927fuX3r5s3ZbP6TD366mK/u3du//+DByy9/tWj91d/93enJWXVzczNzhxocDjczVzU3dcABYmaRGAOTiCOlZjAcvfHm69/97h9+63d/987BwXhnfPDOEV548t5dJjLCFoPMnwNAREyELXe4w0BMIAJQq5a+TC+vDp+eHB+fnV9cXk1ni/miLz1gVauWXGopJaspyAG4OwAiBOEQgkgIIYoIi8QYm6btmm40GrVd16TEEpiwJYDBtZb1pl/M55fTy7PT0y8eP/74o0/m8/lwNLx9+9ZXvvKVl1566e7du/t392/fuT0ajgBU1dxv1pt+tVpuNr3WAkBCDCIhCAAzY5YY2sACgETSc4FFAK81V1UAxBJjYBFhrlXXq6XC2yYNhqOdvfFkMh4NhzFGIhz8yRGu/Xj/2wc/OHTHFrkWPEcAATh49xjXnr53z7VqzmW9Wl3Nl/PZaj5bz2ebxaysF6rZa6laXLWqFbVetagWA0uI7aAbDgfDUdcN2mEXU2BmN60511xKqTCLHBmwqmWzXs7ni8Vys1zNrq4uLi4up9PZdNbnjYh0g+Hejd3J7t5kZ7KzuzPZ2R3tjAeDUUihmgeR1A5SjCARBrMwCwhwrbWU3K8368163ee+5FJNQRRjiiGFEGNqYtOG2MTYssh3//kDXPubP1u17aDpuq5tYxspMAvdf+cI1x4/uktE7IA6AGLClulysTg9O/3ZBx/+xb/5t7/85S/mV1e5zwQQwIwtUwUgJBwkELNQEGliMxh2DKw26yAynownk8loPDKzy+l0tdnAkZp2d2dccn769Oj84nKxXppaCMGBWkutCnjV56qpq4I5pcQgU0sxjIZDEbGiw9Hw4P7BzZs3u667upr//a8/uri8JOK79/bf+tZbKaVPP/vs/OJCq5qZqgEwh7urmrtWVTM3VQdIiFliEIbAkVIznkzefPON733vj/7p//4dvHD43r6aAhAREMzxHAEOdwdAREyELXe4w0BMIAJQq5a+TC+uvjw8Ojo5O392MZ3OVstFn3u4qWmtpdRiWqop3B3uZlXVtBJT07Rtk2JsJIQgwiIxpKZpBl3XNE2MDYsALkQhiLCwMDMR8Wq1PD979g+fffrhhx9OL6aTyXj/4ODrX//a/YOD3b0be7s7k8lOjEHNtZa+z+vNerVcrdbrkrO5NSnFlJqUWAQAEQdOgYUIIYTUtjGlIOKwUnrdMmNmCYFFhFlV15u1u6WmHY4Gu7s74/FoOOxijO7YMseDd49w7fDRPQDkCuDg3VNce/rePRCec7hWzbmsV6ur+XI+W81n6/lss5iV9UI1ey1Vi6tWtaLWqxbVYmAJsR10w+FgOOq6QTvsYgrM7KY155pLKRVmkSMDVrVs1sv5fLFYbpar2dXVxcXF5XQ6m876vBGRbjDcu7E72d2b7Ex2dncmO7ujnfFgMAopVPMgktpBihEkwmAWZgEBrrWWkvv1Zr1Zr/vcl1yqKYhiTDGkEGJMTWzaEJsYWxYBCSQIx5Cath00Xde1bWwjBWYhEO7/yRFeOHy0D3UAxIQt0+VicXp2+rMPPvyLf/Nvf/nLX8yvrnKfCSCAGVumCkBIOEggZqEg0sRmMOwYWG3WQWQ8GU8mk9F4ZGaX0+lqs4EjNe3uzrjk/PTp0fnF5WK9NLUQggO1lloV8KrPVVNXBXNKiUGmlmIYDYciYkWHo+HB/YObN292XXd1Nf/7X390cXlJxHfv7b/1rbdSSp9+9tn5xYVWNTNVA2AOd1c1d62qZm6qDpAQs8QgDIEjpWY8mbz55hvf+94ffet3/7M7B/vD8ejgnSO8cPhoHwRzPEeAw90BEBETYcsd7jAQE4gA1KqlL9OLqy8Pj45Ozs6fXUyns9Vy0ecebmpaaym1mJZqCneHu1lVNa3E1DRt26QYGwkhiLBIDKlpmkHXNU0TY8MigAtRCCIsLMxMRLxaLc/Pnv3DZ59++OGH04vpZDLePzj4+te/dv/gYHfvxt7uzmSyE2NQc62l7/N6s14tV6v1uuRsbk1KMaUmJRYBQMSBU2AhQgghtW1MKYg4rJRet8yYWUJgEWFW1fVm7W6paYejwe7uzng8Gg67GKM77r9zhGs/3v/2wQ8O3bFFrhXPEZ6jg3ePcO3pe/fc1ErJ6/V6frWaX63ns9Vi1i9mZbOyWlSz1VJrVdW+1j7XvmoxB0tsu6YbdIPBaDQejUdNm4jhaiX3tS+lVJgGFnLUXPr1ej67Wszny8VyuVhcXV0tV6v1em2mxJJSSG3bdM+NdyY3bt26cWNvvLvbpFTNWGLTdTEkAEzEIsLMIoC5ai79er3ebNZbJeeiFcQppRhTkBSbNqQmpjaGxBK++89fwrW/+bNN23ZN13WDNqZIgVjo/jtHuPb40V0iYie4AyBsOczW69X5+fnPf/bzf/dv/u0vPvz55XTab9aBtwgEOKwqARxEiBkgJiJumjToWhHp8yawdIPhaDQYjkYhxFIzHBITM8wwm80eP34yu7qsBmIEEXfkPlervmVubtXMVYk5hkhMVmpKcTIep5jctO0Gt2/dHO/sDLtuuV5/+eTLZ+eXq9V6d3fn1Tde69ru+Pj4an5VSi21umHLHQ64u16rqm5m7kTEzEGEQO5om3ayu/vNb775ve/98X/zv/0jvHD43l01AyAiIDJ3AExk/hwAImIibLnDHQZiAhGAWrX0ZXpx9eXh0dHJ2fmzi+l0tlou+tybm5tWLVqrajWr7q5uplpVS8lBpO2uta2ESERCzCwSQpNSTE0KjQQCIYrEmFKMshUkBOn7PJ1efvHFFz//+c+n0+l4NLp7d/+VV17+/6iCl1/N0us+zL+11nvZe3/fudS9T1UXKcuULVkk1U22YxlQrEgiBxmwGvQ4kwyCAJlkmFkc2B4kQeIMA8jIvxCB1UASW4FMWmJgkS1RYkuWIEsUu5rVpy5ddc533Xu/77vWyneOWAM/z/3792/evLk8Os4pEcHMamullHmatrv9fr+bpsndU4w5577vJUQATBJYmIWJQojd0MeUQhAAtZam1c2JICGQiDCr6jRPIHS5G5bDyenx0XLZ912MwR0H5nj4/jmuPX18BoDcANx//zmuffqtMxCuONzUai3jOG7W+8163Kz229W8XdVpb62qFmu1taaqc2tzaXPTag6W2PW5H/phWC6PlkfL3CViuFotc5trrQ2mgYUcrdR5HDer9Xaz2W13u+12vV7v9vtxHM2UWFIKqetyf+Xo5Pjm7ds3b944Oj3NKTUzlpj7PoYEgIlYRJhZBDBXLXUex3GaxoNaStUG4pRSjClIirkLKcfUxZBYAljAIhxD6ruuz33fD11MkQKxEAgPvnGON54+PoM7AMKBw2wc969evfrojz/6f/6v//tPfvjRxeXlPI2BDwgEOKwpARxEiBkgJiLOOQ19JyJzmQJLPyyWy2GxXIYQaytwSEzMMMNqtXry5JPV+qIZiBFE3FHm0qz5gbm5NTNXJeYYIjFZbSnF46OjFJObdv1w5/ato5OTRd/vxvEnn/zks1cX+/14enrycz//d/quf/bs2XqzrrXV1txw4A4H3F2vNVU3M3ciYuYgQiB3dLk7Pj39xV/8ha997etffufLt966t1gu7z86xxtPH78FInMHwETmVwAQERPhwB3uMBATiAC0pnWul6/XP3l6fv785avPXl9erva77Vxmc3PTplVbU21mzd3VzVSbaq0liHT9ta6TEIlIiJlFQsgpxZRTyBIIhCgSY0oxykGQEGSey+Xlxccff/zRRx9dXl4eLZf37r31sz/7t+7fv3/z5s3l0XFOiQhmVlsrpczTtN3t9/vdNE3unmLMOfd9LyECYJLAwixMFELshj6mFIIAqLU0rW5OBAmBRIRZVad5AqHL3bAcTk6Pj5bLvu9iDO548Ogc1z586737//KpOw7ITXGFADjw4NE5rn36+MzVrLUyjeN6PW43++1q3Kzm7aqNe9VqrbRWW61VWy1tbK00bWpOIjnn1KWhPzpantw46fseBJjWUlsprTQ3ZRI0raVM+/1qtdquN/vdbp4L3JqqtmYOJqijaTV3MPWLxe07t2/duX0wLBYGFpGUeg4CIiYWFhYSZgCmtdQyTvt5muZSaq2mCpCEGEKMIYaUY+pj6jgkkfC1f/Y2rn33f5q6fsh9n4cu5UBCJPTw0TmuPXl8j4gYRA64OwAzgo/TuF6t/vSjP/ntf/WvP/rhR5+9/Gwcd0zMTELsbk2VgBiEwIDhWhCRIEwMIATJuRuGflgs+y6LxJSCxNhqvVhdfvby1dOnT6d57oY+pURMpjpOY6vN3c3dHDBzODHHGAhoTVOMp0dH/TDEEKIEDrJcDDdv3QLw6tXry9VmN45B5MatmyKy3W73+900TbUqAHeAADAAUzPV5mpNDQ6AQMRMIDcMQ3/z9q0vffFLX//61/7L/+M9vPH0t+4pDCARJiLHT5lfAUBETIQDd7jDQEwgAtCa1rleXqyffvr82bOXr15frC9X2812rpO5mTVVNW2qzayZW1M1VTNtqinGxWLoh0XfDTEGAOYOc4BCCBJijCkEEZaYQp+7GASAmram0zTudruXL19+/Nc/3u62IaYbpyf3rz14++3jo2N3U3M3VTNtbZymcbffT+O426tbCCGl1HVdjIkIQsIU5IA5xpT7PucUYyBCba1phTuIJEQWYYKazqUwoev6xXI4OT0eFou+yyGIO/7Gg0fnuPb08RkAggO4/+gZrj19fEb4KVez1so0juv1uN3st6txs5q3qzbuVau10lpttVZttbSxtdK0qTmJ5JxTl4b+6Gh5cuOk73sQYFpLbaW00tyUSdC0ljLt96vVarve7He7eS5wa6ramjmYoI6m1dzB1C8Wt+/cvnXn9sGwWBhYRFLqOQiImFhYWEiYAZjWUss47edpmkuptZoqQBJiCDGGGFKOqY+p45BEAliYo0gIsev6Ifd9HrqUAwmREAMPHp3jjacfnJED7g7AjODjNK5Xqz/96E9++1/9649++NFnLz8bxx0TM5MQu1tTJSAGITBguBZEJAgTAwhBcu6GoR8Wy77LIjGlIDG2Wi9Wl5+9fPX06dNpnruhTykRk6mO09hqc3dzNwfMHE7MMQYCWtMU4+nRUT8MMYQogYMsF8PNW7cAvHr1+nK12Y1jELlx66aIbLfb/X43TVOtCsAdIAAMwNRMtblaU4MDIBAxE8gNw9DfvH3rS1/80te//rVf/NKXbt270y8W9x+d442nj98iIsdPmV8BQERMhAN3uMNATCAC0JrWuV5erJ9++vzZs5evXl+sL1fbzXauk7mZNVU1barNrJlbUzVVM22qKcbFYuiHRd8NMQYA5g5zgEIIEmKMKQQRlphCn7sYBICatqbTNO52u5cvX3781z/e7rYhphunJ/evPXj77eOjY3dTczdVM21tnKZxt99P47jbq1sIIaXUdV2MiQhCwhTkgDnGlPs+5xRjIEJtrWmFO4gkRBZhgprOpTCh6/rFcjg5PR4Wi77LIYg7Hjw6x7UP33rv/r986o4DcjMcEByA48Gjc1z79PGZq5lqGadxux6362m72a9X82ZV553WalpbK7WV1lqtOtdW1aoZSDgkSSl3uR/645PjrsvEDFettZXmqlADM5rWed7v9uv1er/djuMIQ8pJmNXNHUTUtI3TNB+UIimc3rhx69atO/fuLo6OJQQJkUMSFhZhJhHhA4KZaWulTPM811pqq27mDjpgYRYJKcYUUh9j4phE0tf+6X1c++7/PPf9kPq+H7qQAgmI6eH757j25PE9ImIQOeDu5oATvMzzZrv98z//89/57d/+6I9++On5p7vNjhhCILDDWlMCUkrCbGZuijeEJcSYYowp5Zxy7rsudV0OMQWRaZ5fv359cXGxWq2aad/1kiLcSq3jbiy1qJm542+Yc5CUEgFaa4zx+Gi5XC6GYQAw7sec8+07t1Pqxv24H8dxHlUVxKZWW5nnMo5TqxVXGExELEwGmLmpNlU3c3c6wDXHYnl87+zeL335y7/+G7/xX/zvX8YbT3/rnsHBxHTF8VPmVwAQERPhwB3uMBATiAC0pnWuq4vN0/PnL1+8+uzV69VqtdtspzKZqVlTbU2baVNVc22q2po7mJFzWiwW/TD03SLGYOaq2lozdWZikRhzjDEFiTHEGIXJ3LXpPM+1lNrq5Wr17Px8u9sJ8bBc3r596627995++Pby6Miv4IpZMy2lTNM8jeN+3FtTEg4SYoohRCIwiZAICxGFGLu+zznFmJihpmoKgFgkSBABEdxrK8TSd3m5XB6fHg/DkHMMQdzxNx48Ose1p4/PABCu3H90jmtPH58BIFxxNVMt4zRu1+N2PW03+/Vq3qzqvNNaTWtrpbbSWqtV59qqWjUDCYckKeUu90N/fHLcdZmY4aq1ttJcFWpgRtM6z/vdfr1e77fbcRxhSDkJs7q5g4iatnGa5oNSJIXTGzdu3bp1597dxdGxhCAhckjCwiLMJCJ8QDAzba2UaZ7nWktt1c3cQQcszCIhxZhC6mNMHJNIAglLEI4h5b4fUt/3QxdSIAExMeHBo3O88fSDM3LA3c0BJ3iZ5812++d//ue/89u//dEf/fDT8093mx0xhEBgh7WmBKSUhNnM3BRvCEuIMcUYU8o55dx3Xeq6HGIKItM8v379+uLiYrVaNdO+6yVFuJVax91YalEzc8ffMOcgKSUCtNYY4/HRcrlcDMMAYNyPOefbd26n1I37cT+O4zyqKohNrbYyz2Ucp1YrrjCYiFiYDDBzU22qbubudIBrjsXy+N7ZvV/68pd//Td+4xe/+IvHt272fX///Wd44+njt4jI8VPmVwAQERPhwB3uMBATiAC0pnWuq4vN0/PnL1+8+uzV69VqtdtspzKZqVlTbU2baVNVc22q2po7mJFzWiwW/TD03SLGYOaq2lozdWZikRhzjDEFiTHEGIXJ3LXpPM+1lNrq5Wr17Px8u9sJ8bBc3r596627995++Pby6Miv4IpZMy2lTNM8jeN+3FtTEg4SYoohRCIwiZAICxGFGLu+zznFmJihpmoKgFgkSBABEdxrK8TSd3m5XB6fHg/DkHMMQdzx4NE5rn341nv3/+VTdxyQuwFwXDHHw0fnuPbp4zNXN9Oyn8bdZt6ux+123K7GzaqOu9ZmbdVqaa2qalWtas1MFcbMEimGEK/kHCWEIAwCmsIM7gAxkTWtc5nHcbvbzeNYS40iwzBIiOoKImFWs2mex/1+vd2oWR664+PjW7duH52c5L4PMREJhyAhxRhDEAbMtLZa53kupdZipgCYSTiSkDuIWCSEmELsJCYJiUS+9j/cx7X/739tfdd3fZ+GHKOAcfC5bz7DtSeP7xGIicjh7jAHHPDW6jiO/+Ev/uI7v/Nv/vgHf/Tjj5+sLy4BJ1xxmFVl5q7PkUVNTd1hcJipSOyGLqUUJYYYSCiySIxE5PBW6m6/V9UYIwhVW1M182mctrvtNE1am8IBEEiEJYSUkoCaKTMv+nx0dHR6cgLD68sLADdPb3R9r4Cp1lbned7tx1pmd9RaxqmoKgAiJpYgwiIEcsBdm6qZu5m7ExEAAh+fnDz83MN333nn1/6zX/vH/9vP4Y1Pf+ueM4FARAAcP2V+BQARMREO3OEOAzGBCEBrWue6utycP3v54uXr169er1ar7XY7T1Ozqq3VVk1rbc2smWrTVlsjor7LXdcNi6E/6HqW4Oa1tTqXWhvgzJJzjinnnARo2lzV4HjD3Xfb7fn5+Xa3E+a+77dnplIAACAASURBVE9OTm4c3Lx5tFym3MUYhJlA5t5anaZ5LmWeJ1WFOw6ImBgHRAIBGIAE6XKKOaeUgrDDABAzi4SYhImYAbhrCKHr+uXR8uT0uB/6GIIIAzAHEx48Ose1p4/PzCGMg/vfOMe1Tx6fMeGAHK5upmU/jbvNvF2P2+24XY2bVR13rc3aqtXSWlXVqlrVmpkqjJklUgwhXsk5SghBGAQ0hRncAWIia1rnMo/jdrebx7GWGkWGYZAQ1RVEwqxm0zyP+/16u1GzPHTHx8e3bt0+OjnJfR9iIhIOQUKKMYYgDJhpbbXO81xKrcVMATCTcCQhdxCxSAgxhdhJTBISiTAn5kASY8p913d9n4Yco4BxQEIPH53jjacfnJHD3WEOOOCt1XEc/8Nf/MV3fuff/PEP/ujHHz9ZX1wCTrjiMKvKzF2fI4uamrrD4DBTkdgNXUopSgwxkFBkkRiJyOGt1N1+r6oxRhCqtqZq5tM4bXfbaZq0NoUDIJAISwgpJQE1U2Ze9Pno6Oj05ASG15cXAG6e3uj6XgFTra3O87zbj7XM7qi1jFNRVQBETCxBhEUI5IC7NlUzdzN3JyIABD4+OXn4uYfvvvPOr/1nv/Z3fuHnl8dHKef733yONz794AyA46fMrwAgIibCgTvcYSAmEAFoTetcV5eb82cvX7x8/frV69Vqtd1u52lqVrW12qppra2ZNVNt2mprRNR3ueu6YTH0B13PEty8tlbnUmsDnFlyzjHlnJMATZurGhxvuPtuuz0/P9/udsLc9/3JycmNg5s3j5bLlLsYgzATyNxbq9M0z6XM86SqcMcBERPjgEggAAOQIF1OMeeUUhB2GABiZpEQkzARMwB3DSF0Xb88Wp6cHvdDH0MQYQAPHp3j2odvvXf2m08BmIPcDYDjijkePjrHtU8fn7m6mdd5mjabcbuZdutxuxm3qzrttMxaS6tz0+ZNm5uaq6MZDAQRsLDwgQiHwMQsDBjIHUQCEKg1reM0jdM0jVqrmecYl0dHMcZqykQhBBC1Wnf7/cV6NZcCRu76G6eni+OjflhITMxBQsxdH1OKQRiorVwZp7kWaw0ABwoxppCIxQ1gMAuFFEIKIVEIzPHX/8k9XPt3/0Jz13d9l7ocozg7ET73zee49uRb94iIicjh7mYOOMPVtM7zj/7yR9/59rd/8Ad/+Jd/9ZcXr17DDeYAHGaqQeJiMYgE09ZqczczbapBJHd9ylEOSEiYRWJKTAS4qramIciwGMx8vdnO89RUp3HcrNf7adLazA1EBOIgKcTcZWI2VSbKXVouFzdOTuG4uLy0WrthyDmFmEDUWpunstltSikAVLXMrZkSwEQsUSSEKEEExOZmqqbamjrg7kTELLdu3frC3/7CO1959x/96q/+5//sPt749FtvgQgEd8cBEa6ZXwFAREyEA3e4w0BMIALQmta5ri+3z56//Ozl69evLlfr1Wa7neaxtaqt1lpaq7VV09asadNaKwkPfe66ru+63PVd7kMMMK/a5qmUWrU1ZslXuq7PQcT0SqvV3AC4u6leXl7+5Omn+/0u5+74aHnr1q3T09PFcrkYFl3fp5SiCIuYqprWWpuqmZup24GrqrnhwGEKmDdVYY4pxxRS7kIQBjhQCDEECSmJCIj4CmJKQ98vj5YnJ8f90EkQJjbHARMePDrHtU++dQZAGAf3v3GOa588PmPCATlc3czrPE2bzbjdTLv1uN2M21WddlpmraXVuWnzps1NzdXRDAaCCFhY+ECEQ2BiFgYM5A4iAQjUmtZxmsZpmkat1cxzjMujoxhjNWWiEAKIWq27/f5ivZpLASN3/Y3T08XxUT8sJCbmICHmro8pxSAM1FaujNNci7UGgAOFGFNIxOIGMJiFQgohhZAoBOYIDiKRJcSYc9d3fZe6HKM4OxFI6OGjZ3jj6Qdn5HB3Mwec4Wpa5/lHf/mj73z72z/4gz/8y7/6y4tXr+EGcwAOM9UgcbEYRIJpa7W5m5k21SCSuz7lKAckJMwiMSUmAlxVW9MQZFgMZr7ebOd5aqrTOG7W6/00aW3mBiICcZAUYu4yMZsqE+UuLZeLGyencFxcXlqt3TDknEJMIGqtzVPZ7DalFACqWubWTAlgIpYoEkKUIAJiczNVU21NHXB3ImKWW7dufeFvf+Gdr7z7j371V7/whS/koQsh3v/mc7zx9PFbOCDCNfMrAIiIiXDgDncYiAlEAFrTOtf15fbZ85efvXz9+tXlar3abLfTPLZWtdVaS2u1tmramjVtWmsl4aHPXdf1XZe7vst9iAHmVds8lVKrtsYs+UrX9TmImF5ptZobAHc31cvLy588/XS/3+XcHR8tb926dXp6ulguF8Oi6/uUUhRhEVNV01prUzVzM3U7cFU1Nxw4TAHzpirMMeWYQspdCMIABwohhiAhJREBEV9BTGno++XR8uTkuB86CcLE5nj4/jmuffjWe2e/+dQcB2RueMMcDx+d49rTD85gUPU2l3m3Gbebcbued5t5v63jtpXSyljLbK02VzdXwI0aoA4DAzCAiUIUEWahA3YcMK64WSt12k9lmuZ5NrPA0nV5sVgECc0URH3XSQggmuf5cr3a7/dFm4gMy0XfD7HrYspBckypG4acU44RQKmlzNM0zbUVNyPmGCTEFIIEFmIBEUiIRSSSJBYBh1//7+/i2r/7F5q7Lqcu9lmiMDsJPv/NF7j25Ft3iZiJyOHuZg44A+aqrf34Rz/63W//2z/8gz/493/2Z5+9+MxNAceBGwwpp+VyEUOs86yqrbaqVVVB4CBBoogEERbJXbc8WuaUSIRwRURiDOM0f/bi5Xa3c7dpGjeb7TRNqmpmAIiZiFNOXdcFkaYahHLqhiF3/cCgUksrVU2JJaZMRK0elFrVXd3QTLWpmTuciAOLxBRTDCLMBEBVm6qptqbuTiQpprtv3ft7v/Dz73z1q7/yK7/yn/53x3jj08dnILi7uQNgIhABML8CgIiYCAfucIeBmEAEoDWtc12vti+ev/rss9eXF6vL1Wq73c7jWLWUWmqZay2qrWltqtq01gJCTgchpZRzl7s+SmRhU53KQdVaQRRj7ruu67th6BeLBdy3m81+3M/zPI7jdrd78fz5X/3or6dpunnjxt17d3/m85+/c+fOsFjknEUkXknCpOauagADMSVmMvOmWkoxUwDadJ5KmapqM3cOEmPMKYUYJUiMklOKKeecOEQiBJEQJeW8GBbL5eL49KjvOxZhInMcMOHBo3Nc++RbZwCYcfDgG+e49snjMyb8lEHV21zm3Wbcbsbtet5t5v22jttWSitjLbO12lzdXAE3aoA6DAzAACYKUUSYhQ7YccC44mat1Gk/lWma59nMAkvX5cViESQ0UxD1XSchgGie58v1ar/fF20iMiwXfT/ErospB8kxpW4Yck45RgClljJP0zTXVtyMmGOQEFMIEliIBUQgIRaRSJJYBByYI7GIhBBz7rqcuthnicLsJGDhh4+e4Y2nH5yRw93NHHAGzFVb+/GPfvS73/63f/gHf/Dv/+zPPnvxmZsCjgM3GFJOy+UihljnWVVbbVWrqoLAQYJEEQkiLJK7bnm0zCmRCOGKiMQYxmn+7MXL7W7nbtM0bjbbaZpU1cwAEDMRp5y6rgsiTTUI5dQNQ+76gUGlllaqmhJLTJmIWj0otaq7uqGZalMzdzgRBxaJKaYYRJgJgKo2VVNtTd2dSFJMd9+69/d+4eff+epXf+VXfuXzn/88BWHhB998gTc++dY9AEwEIgDmVwAQERPhwB3uMBATiAC0pnWu69X2xfNXn332+vJidblabbfbeRyrllJLLXOtRbU1rU1Vm9ZaQMjpIKSUcu5y10eJLGyqUzmoWiuIYsx913V9Nwz9YrGA+3az2Y/7eZ7Hcdzudi+eP/+rH/31NE03b9y4e+/uz3z+83fu3BkWi5yziMQrSZjU3FUNYCCmxExm3lRLKWYKQJvOUylTVW3mzkFijDmlEKMEiVFySjHlnBOHSIQgEqKknBfDYrlcHJ8e9X3HIkxkjofvn+Pah2+9d/abT81xQOaGN8zx8NE5rj394MwcUNS5zPvttN2M203Zbep+W+exTmOZx1qmVou6ujtAClJ3NTTzZq5mDEpdDEFYiIkIxAATwd3U6lzG/X4eZ9XGoNTlvrtCzK01BuWhTzGGEEqp6816N+6nWkBIOceUQkwh5hhz6vq+X3R9SjEBVmuppcylmCngzHIQRIgpiLBEYXEIiIgjSCDCLL/+T+7h2nf/l5pyl/IVScIBQfjz//gFrj351l0iZiJyuLuZA844MDV78tc//t3vfOf73/vwT//0T148ew5XGMAgYgL6nI+OjiRImUqrcymlqpqpAXyFmOSAGCl1R8dHOeeYUopBQhQmVV2vNy9evNhutgBKLbvdbp6mZuoOEAgsTDGlruskBDMLzF2XYxAWIYKIqHkpxc1YIoBWqsEZZHBTdTOAAbgbiFlCCJJSEolBBEBTNdNaalM1U2bpu/7+g7e/+KUvvvuVr/yDf/jL/8l/m/DGp4/PQHB3VQNAwkwEwPwKACJiIhy4wx0GYgIRgNa0znW92r16efHq1euL16v1er3dbcdpX2upZZ7LXGtprao2VW2tlloBj0HCtXzQDTmlGKOZ19ZqKXMpcMQQc8790A99vzxaqtnlxeV2u95ut7trn7189eSTJ6XMp6end+/effjw4Z27d4+Pjrq+T/EgxRhArK02VQAhxKHvQwxmrq2O41hbIyJtOo9lnuZ5LmoKpigh5pRTDDGknPucc9/lro8xgDgEiTF0XdcP/dHR8vjkuO+zCANkjgMmPHh0jmuffOsMADMOHnzjHNc+eXwGAuOKOaCoc5n322m7GbebstvU/bbOY53GMo+1TK0WdXV3gBSk7mpo5s1czRiUuhiCsBATEYgBJoK7qdW5jPv9PM6qjUGpy313hZhbawzKQ59iDCGUUteb9W7cT7WAkHKOKYWYQswx5tT1fb/o+pRiAqzWUkuZSzFTwJnlIIgQUxBhicLiEBARR5BAhFlAwhxIYogp5S7lK5KEA4IwCz98/xneePrBGTnc3cwBZxyYmj356x//7ne+8/3vffinf/onL549hysMYBAxAX3OR0dHEqRMpdW5lFJVzdQAvkJMckCMlLqj46Occ0wpxSAhCpOqrtebFy9ebDdbAKWW3W43T1MzdQcIBBammFLXdRKCmQXmrssxCIsQQUTUvJTiZiwRQCvV4AwyuKm6GcAA3A3ELCEESSmJxCACoKmaaS21qZops/Rdf//B21/80hff/cpX/sE//OW3337byUH04Jsv8MaT37oLgISZCID5FQBExEQ4cIc7DMQEIgCtaZ3rerV79fLi1avXF69X6/V6u9uO077WUss8l7nW0lpVbaraWi21Ah6DhGv5oBtySjFGM6+t1VLmUuCIIeac+6Ef+n55tFSzy4vL7Xa93W531z57+erJJ09KmU9PT+/evfvw4cM7d+8eHx11fZ/iQYoxgFhbbaoAQohD34cYzFxbHcextkZE2nQeyzzN81zUFExRQswppxhiSDn3Oee+y10fYwBxCBJj6LquH/qjo+XxyXHfZxEGyBwP3z/HtQ/feu/sN5+a44DMDdcMcOBz3zjHtaePz+Awg9Y67Xbjbjvv1vNuq+Ou7Pdl3tVpnKZ9rcVM3QAmBZp5VatFq2rTxiLD0OUUWZjpijAzERzedJ7LOO5rKTCPISwWi5RzCAHutVYQxZRijCxBta53u2meSqsAUkoiYkQSUk5D7vt+GLrUpSREqE1Nq6q6OzGIiInNzVRZpMu9SDSHg0ACYiNhlq//0/u49p3/cYop5ZxDTimFlEJI/Pl//BLXPv6tOyzCRORwdzMDnEEEd/iPf/zxd7/9ne9/73t//MMfvnz+Aq4AmESYiWnouuPjYxEZx7FM0zyXqgoGHQgLE4vAfC6VGDl1Xd/1Xdf33TAsAGy328vV6sWLF9M4ElFTnaZJa1M3HBAOCBxjzF2OEsyNhRddJuZpmgD0fR9jdLOmXmv1AzNiZhE3a60BFEIUJjOAwSwSYoohSOQgANy0Vq21tlZa0xjjYlg+fPvhL33l3Xe/8u47X/3qV/4bwhufPj4DQc1MFQCLMDMA8ysAiIiJcOAOdxiICUQAWtM61+16/+rlxauLy/Xler3ebHfbcdqXMpd5nstca1GtTZtq09Zqbe7KLCIUQsg5d/3Qd33OmUVa01rqNE9mxhxSSP2QQwxuXmrZbNab9Xq1Xpd5Nsc8jZerVS01pjj0/dHR0c2bN2/fuXN6erpcLHLXRxFzm6a51qJmKaXlchlDBDDP03a7q63iwGDqtdRxmmqtBg8iMaWcYsyp7/IwLPq+z30fUyJCCBJj6ro0DMPR0fL45Dh3WYQAMndcoYfvn+PaJ986A8CMgwffOMe1Jx+cASCAHXCYQWuddrtxt51363m31XFX9vsy7+o0TtO+1mKmbgCTAs28qtWiVbVpY5Fh6HKKLMx0RZiZCA5vOs9lHPe1FJjHEBaLRco5hAD3WiuIYkoxRpagWte73TRPpVUAKSURMSIJKach930/DF3qUhIi1KamVVXdnRhExMTmZqos0uVeJJrDQSABsZEwC0iIA0uUkGJKOeeQU0ohpRASk8jn3n+GN55+cEYOdzczwBlEcIf/+Mcff/fb3/n+9773xz/84cvnL+AKgEmEmZiGrjs+PhaRcRzLNM1zqapg0IGwMLEIzOdSiZFT1/Vd33V93w3DAsB2u71crV68eDGNIxE11WmatDZ1wwHhgMAxxtzlKMHcWHjRZWKepglA3/cxRjdr6rVWPzAjZhZxs9YaQCFEYTIDGMwiIaYYgkQOAsBNa9Vaa2ulNY0xLoblw7cf/tJX3n33K+++89WvvnX2lruD8OCbL/DGx//nbQAswswAzK8AICImwoE73GEgJhABaE3rXLfr/auXF68uLteX6/V6s91tx2lfylzmeS5zrUW1Nm2qTVurtbkrs4hQCCHn3PVD3/U5ZxZpTWup0zyZGXNIIfVDDjG4ealls1lv1uvVel3m2RzzNF6uVrXUmOLQ90dHRzdv3rx9587p6elyschdH0XMbZrmWouapZSWy2UMEcA8T9vtrraKA4Op11LHaaq1GjyIxJRyijGnvsvDsOj7Pvd9TIkIIUiMqevSMAxHR8vjk+PcZRECyNwfvv8M1z58672z33xqjgMyN8ABGOCgz33jHNeePD4jx4HVVsZp3u/n3Wbabebtquy3ZRznaTdP+1qLN3O4AQpX89a0Nqt2hYMMfR9TCMzEJHTAQgR3bVprnaZJW2NQDLFfDClGELnqXIqZhyAhBgnB3Ocyz6VWawBiCGAGMYeY8yLnvutyzDGGQAQzh6sbACNmEODm5k2VmFLqJASAHQQIiMECkq//0/u49jv/fJdySjnnvstdjDnEKJ/75gtce/Ktu0TMRORwdzMHnAGCO/yTj5989/d+7/u///0f/OAPXzx7TgABzGAWJnS5WywXQaSWOpepzE1dQQSAGCAmoKlO0wQgxZS7rh/6vu/6rlfV1cF6vb5czWUmkJmWUlTV8R8JIilliYGAIJK76I79fg9gMQwxRiJqqnOp1gwAB4kiAFSViHJMJOLqYGKRGGKIUUSIyAE3bVWb1lpbrTWEeLw8/pmf/Vvv/f33vvzOu1/88pd+6b82vPHp4zMQ3F1VHSAmImYi8ysAiIiJcOAOdxjARLjSmtW5bjf7i1er1xer1cVqvdlut+txHGubaylznVurqs1MD1prtRYzIwIzHUiIfdf3Xd8NfQgBjtraPE2tKROLxL5PTFy1juO4Xq+32812u1VVCQFAKUVbUzMGS5DFYrh9+/bp6Y2T05PFsJAgAOZpbtpcLcQwLJYpJSK0pvvddi5FVd3cFbW2eZpqawYXkT53OafYpb7LXdfnvu+6nFJikRBiSiF33dFysVgulkfLnLMIADJ3XKGH75/j2tPHZzggAP7gG89w7ckHZ/gbDnIcWG1lnOb9ft5tpt1m3q7KflvGcZ5287SvtXgzhxugcDVvTWuzalc4yND3MYXATExCByxEcNemtdZpmrQ1BsUQ+8WQYgSRq86lmHkIEmKQEMx9LvNcarUGIIYAZhBziDkvcu67LsccYwhEMHO4ugEwYgYBbm7eVIkppU5CANhBgIAYLCABCUiIhCWlnFLOue9yF2MOMQoJP3z0DG88/eCMHO5u5oAzQHCHf/Lxk+/+3u99//e//4Mf/OGLZ88JIIAZzMKELneL5SKI1FLnMpW5qSuIABADxAQ01WmaAKSYctf1Q9/3Xd/1qro6WK/Xl6u5zAQy01KKqjr+I0EkpSwxEBBEchfdsd/vASyGIcZIRE11LtWaAeAgUQSAqhJRjolEXB1MLBJDDDGKCBE54KatatNaa6u1hhCPl8c/87N/672//96X33n3i1/+0p27dx0O4ME3n+ONJ791xwFiImImMr8CgIiYCAfucIcBTIQrrVmd63azv3i1en2xWl2s1pvtdrsex7G2uZYy17m1qtrM9KC1VmsxMyIw04GE2Hd93/Xd0IcQ4KitzdPUmjKxSOz7xMRV6ziO6/V6u91st1tVlRAAlFK0NTVjsARZLIbbt2+fnt44OT1ZDAsJAmCe5qbN1UIMw2KZUiJCa7rfbedSVNXNXVFrm6eptmZwEelzl3OKXeq73HV97vuuyyklFgkhphRy1x0tF4vlYnm0zDmLACBzf/j+M1z78K33zn7zKa6RuQJu7g4C0ee+8QzXPv7WGQEEwMyr1mkad5v9+nJ7+Xpar8q0m6d9ncdaK8zcTOFqXpuauxnADDkIKYoEESZiOmDHgZm5qqnV1uDORMISY2Rmd2+1zvNcVQFIkJwSB3GgmTVVwPlAQogxxJRSLzHFkDhIDARiXDG4wwAY2HFgcBwQibCIsIAjSJgFTKD46//kHq79v/98nVPOfT8slv2QYmIK/PDRM1z75PFbTASAHAfuDjiZO+zg05/85MPf//B73/v9D7//4fn5eRTCgQPmYIoiOSWJUQAzV29q3tTMVAFXraqmrTYVpi53XZ+7fhGDmPk8TZvtej/Opq02tdaaqtamrgQG4GYOuCuRpBhTigcsQgKHmyoAZiEmONzd1MzATEzMQYQIALOkGEFkqsSSQooxSooEMlVzNYO7mWktbRxHCeHmjVt/++/83D/85V/+pa+8+3N/9+9+6b9qeOPTx28BBIK7q5ubE4iEAbg7ACJiIhy4mzrgADEOXNXmue03+8uL9cXr1cXFar1ar7ebadq3Vg9Mm7rCzVzVrNU6l2LaQIBbUyWQhNjnrh+GnBKYzazOpTaFQ0RyThwYoHHcv3r9ar/btaYg5JRA5OZNVVttqqbKIothcXS0PD45WS4WuetiiLjiaibMOXcxpRQDgFLKNM/jONZSrVqtbS7F3JkppjQMQ991OceYEovEEEJKKaeDnFLMaRj6o+VysVz0Q59yZDqAOw7M8fD9c1x7+viMCA4H/ME3nuPakw/OcOD/P1dw82vZed2J+bfWet93733uuVUs8as+JEq2bJEhKRpxBMRuyVQ3kEliFAXbnUajgwRBBplklEkmAYJ8DDJwgIwCw/kLAgSIoCoEGaQRoCmy4W6XPmhbsmSLDYpksUgWq+4995579tn7fdf6Zd9DlRDkeUBCAAEQwep1vx8vzndnp9vTx/uzzby/mPa7Oo21VkQwwkEP1uZBRgCqsEUq2SyZqYjKQolFRNA9PGprIFXE1HLOqkqy1TpNU3UHYMm6UjQZgRbR3AHqwlLKOeVSymC55FQ0WU4CUVwKkAgAASUWAWIhYqZmpgbNEFM1qEAyYAEhVMy60nXDsDpaD6uSi0pSFbl1+wGeuH/3hhALkgAlSMTiow8/vPev7v3rf/2v7v3lvQcPHmQTLAgEoZLNulIsZwMi6GwebB4R7gDdq3t4q81Npe/6fuj64Sgni+C0359vz3bjFN5q82ituXttThcoAEYQIF3ESs6l5IWaiYFguANQNVEBQTI8IqAqKqrJTASAqpWcIRLuolZSyTlbyQIJ96BHgIwIr3Mbx9FS+sK1p7/6td/+/d/7vd/53X/7t1988elnnsFCcfM7H+OJ+3euO4NBgYgpAJIARERFsCDDCRAQxYLuMU1td747PTk7ebw5Odmcbc7Otuf7/a61ughvTgcj6B7Rap3mObxBAEZzF4ilPHT9sFp1pUA1Iuo01+YgzKzriiYFZBx3jx4/2l1ctOYQdKVAhMHm7q0293BXs6PV0fHx+srVq+ujo67vc8q4RI8w1a7rcyklJwDzPO+naRzHOteoUWub5jlIVcmlrFaroe+7LudS1CynlEopXVl0peSurFbD8Xp9tD4aVkPpssoCJG698QAH965/4/k//1BlAQk2gE4CAtEXbn+Mg/e+d0MCqtBgePg07S+2F5uTzeOH49lmGrd1v5unfWsVEYxwhjtreBAQFU0p5ZQMClNRlYWKgAiSEYxgRJAADCKqIoIDb23aT9UbSDHruy6VLKYAqrcgVcRSyrlLpe+6LqUsZqKWTCAqAgHA4AIBEIAAECEUBEQ0ZVMDDGpQg9i/99/dxME//x/OulL61dHx8bpf9ZZFk9y6/QAH9+/ewIEQTxAMRnjEx/c/eudHP773l3/5F//yLz766EMVFSDcgRComuRcslnKWVUBtPB5rq25I8K91tkXwaTW9V3f913fq0qrbbe42M11do9wr968NkYAUFUcMMJJFTG1lFMqOZkSBEgsGCQABomFCEQgZrowVRVRs5wyIK25qXV9X0qxZCTaPDsDC2IxzfN+HC3lp5955sUXX/zmt7752u/8zm/81m+9/J9NeOKj7z0PEYgAcJLuBMRUIGQAEFEVARAkPPCEAt6izm27HU8fn52ebB4/Pj1bnJ/vymoHzAAAIABJREFU97vWqtcWbBEBEKDTW2vTfvJwFbbwOs/hVLVcymq1KqWYJQSnOnvzCKqI5ZRMoTLu96cnj8f9PpnlnEspZhYkI1pr81z34+iMZDasjr5w7an1+nhYDV3X5ZQgEu6ApJRyTrkUEa113u/3F9uLeZqjRW3eWoNIStb1/dHR0TD0XVcsmamJWUqWS+m6rh/6oR9WR8OV4+PhaNX3xZKpLLAgsbj1xgMc3L9zXUSIAHjr9qc4eP/uDQAkSEhAFRoMD5+m/cX2YnOyefxwPNtM47bud/O0b60ighHOcGcNDwKioimlnJJBYSqqslAREEEyghGMCBKAQURVRHDgrU37qXoDKWZ916WSxRRA9RakilhKOXep9F3XpZTFTNSSCURFIAAYXCAAAhAAIoSCgIimbGqAQQ1qEIMkQAkRy10p/ero+Hjdr3rLokkA3Lr9AE/cv3tDiCcIBiM84uP7H73zox/f+8u//It/+RcfffShigoQ7kAIVE1yLtks5ayqAFr4PNfW3BHhXuvsi2BS6/qu7/uu71Wl1bZbXOzmOrtHuFdvXhsjAKgqDhjhpIqYWsoplZxMCQIkFgwSAIPEQgQiEDNdmKqKqFlOGZDW3NS6vi+lWDISbZ6dgQWxmOZ5P46W8tPPPPPiiy9+81vffO13fuc3fuu3nrp2DSoQ3HzjAZ746O4NJ+lOQEwFQgYAEVURAEHCA08o4C3q3Lbb8fTx2enJ5vHj07PF+fl+v2utem3BFhEAATq9tTbtJw9XYQuv8xxOVculrFarUopZQnCqszePoIpYTskUKuN+f3ryeNzvk1nOuZRiZkEyorU2z3U/js5IZsPq6AvXnlqvj4fV0HVdTgki4Q5ISinnlEsR0Vrn/X5/sb2Ypzla1OatNYikZF3fHx0dDUPfdcWSmZqYpWS5lK7r+qEf+mF1NFw5Ph6OVn1fLJnKAoubtx/g4N71bzz3Zx+YySWyEgyCEIi+cPtjHLz33RsAVCFBNq/7abw4321OtiePLs43ddzOu908j94qPIhw0iPCw7EwSynlDBWQEIqqiQhEAJLgJY8giYAAqgJAFqpkzPPszQFYTrkrlrOpEmytOQnAzHLuctf1/SrnIqqiJgoRUUBAEgEnA6BgIRAlwAhARY0QQgkTNVH79//Hr+Dg//nvT0s3DKvV+spxv+okiZh86Y0HOLh/90aASiyEgAhAgPQIxsMHH//NX/31vXv33n777Q9/+QHgdHoEAFOYWbZccsolW8oiaBHTNFevQbh7uDd3BlUll1JyKV1WtXCf57rfj/v9OO6neZ7DneEIqNpCTUVVIIwAQFAgoiomqgJBMBhBEE8IVKBmKqoqYqoCqFlSA9QjTK3rS86dpUSy1soILASLaD7up9KVp5955qUXX/rmH3zr1dde+9KXv/zSfzriifvffQ4qkEsEncTnCEYAEFURgQAkPUCIiEIAuEed2247nj4+Ozk5e3yyOdtszrdn0zjWNtdWw1tEAATCyfA2zXN4A+DRpnmmB6Ap5650pStmCWSttTX35gEkEwDBmOZ5t7sIou9K13UlF0sGIEh3n6Zp3O3mefbgahieefaZp65e7fq+K8VSUtEIx0JEVU0tGHWex3G/213UuSLEgxGhqrnkru/X6/Vq6LuupJxFoGZqKZc8dMPqaFit18fr1dF6PfR97pKZiuDXSNx64wEO7t+5LiKAE7x1+yEO3r97AwAJOhaqkCCb1/00XpzvNifbk0cX55s6bufdbp5HbxUeRDjpEeHhWJillHKGCkgIRdVEBCIASfCSR5BEQABVASALVTLmefbmACyn3BXL2VQJttacBGBmOXe56/p+lXMRVVEThYgoICCJgJMBULAQiBJgBKCiRgihhImaqIkWUVMxS6l0w7Bara8c96tOkoiJCm7dfoAn7t+5DkAIiAAESI9gPHzw8d/81V/fu3fv7bff/vCXHwBOp0cAMIWZZcslp1yypSyCFjFNc/UahLuHe3NnUFVyKSWX0mVVC/d5rvv9uN+P436a5zncGY6Aqi3UVFQFwggABAUiqmKiKhAEgxEE8YRABWqmoqoipiqAmiU1QD3C1Lq+5NxZSiRrrYzAQrCI5uN+Kl15+plnXnrxpW/+wbdefe21L335y1euPgUFwJvf+RhPfHT3BkEn8TmCEQBEVUQgAEkPECKiEADuUee2246nj89OTs4en2zONpvz7dk0jrXNtdXwFhEAgXAyvE3zHN4AeLRpnukBaMq5K13pilkCWWttzb15AMkEQDCmed7tLoLou9J1XcnFkgEI0t2naRp3u3mePbgahmeefeapq1e7vu9KsZRUNMKxEFFVUwtGnedx3O92F3WuCPFgRKhqLrnr+/V6vRr6rispZxGomVrKJQ/dsDoaVuv18Xp1tF4PfZ+7ZKYi+NzN2w9wcO/6N577sw/M5BI5EwiCEIi+cPtjHLz33RsAVIEITj7vx/3F+e5sc376eNpupnFb97t5P7Y6I4IMgh4MZ6ioJtWUShZBMADKAoLPkQBIegRJBPGEQCwZGbU1jxARVev6YjmJKoMeHhGyMMu55G4YuiGXAjVRmCwUCJBEBAk6DhSASAAkAIUIqc1BCNRM8x/+6Vdx8M//25OuG/rVcHxlXYYuFYPJC9/5GAf3794IEIQSAkAEIECQjPj0k09++tc/+dEPfvD9t97+5XvvsdUIByCAqCVRyymbJUspGUya+zjNtdbApXAP9wiIItki55xEwUCE17lNdb+72Lc6OwlQISaWcjKznJNAABCI8OYBBkERIej0CAdJEIBARMzUVFRMTQ4AMcuaRYVBMenKkHNWM5CtVvfAQiCCaJzmfTcMzz773EsvvfSt1//glVdfvf7FL774n1zgifvffQ4qkEvEggGA4CICgKhALoGkE6SIqooArUWb2247njzenDw+Oz3dbDZn24vz/X6c57m12VuN8ACBIBnutdYID4S713l2DxCi1pWSc1ZLAFpt7t6aMwKCCJ+9eau1tpRsdXQ0dL3lpKr4HDnN8+7iYjeO8zx3Xf/cc88+dfVq13W5lJySqkYQv0JAPHw6GMfRmwsUFJJmlksZVsP66GgYhtLllAwQTZZTzrl0fVmt109dPV4fr1ero74vmtRU8P9B4tYbD3Bw/851EQEagVu3H+Lg/bs3AJCgY6EKRHDyeT/uL853Z5vz08fTdjON27rfzfux1RkRZBD0YDhDRTWpplSyCIIBUBYQfI4EQNIjSCKIJwRiyciorXmEiKha1xfLSVQZ9PCIkIVZziV3w9ANuRSoicJkoUCAJCJI0HGgAEQCIAEoREhtDkKgZpo1JdEsYpZK1w39aji+si5Dl4rBRARfeuNjPPHBnetKCAARgABBMuLTTz756V//5Ec/+MH333r7l++9x1YjHIAAopZELadsliylZDBp7uM011oDl8I93CMgimSLnHMSBQMRXuc21f3uYt/q7CRAhZhYysnMck4CAUAgwpsHGARFhKDTIxwkQQACETFTU1ExNTkAxCxrFhUGxaQrQ85ZzUC2Wt0DC4EIonGa990wPPvscy+99NK3Xv+DV1599foXv3h8fIUgwFt/9Ame+OjuDWLBAEBwEQFAVCCXQNIJUkRVRYDWos1ttx1PHm9OHp+dnm42m7Ptxfl+P87z3NrsrUZ4gECQDPdaa4QHwt3rPLsHCFHrSsk5qyUArTZ3b80ZAUGEz9681VpbSrY6Ohq63nJSVXyOnOZ5d3GxG8d5nruuf+65Z5+6erXrulxKTklVI4hfISAePh2M4+jNBQoKSTPLpQyrYX10NAxD6XJKBogmyynnXLq+rNbrp64er4/Xq9VR3xdNaip44ubtBzi4d/0bz/3ZB2ZyiZwAEAgIYS/c/hgH7333BgABQPe5zbvduN3szs7H85N5ez7vL6b9ru53rc50B7gA4QB0kTVdElUwiAVBMBgkQfBXgpcQxCUCAkgg3JsTpmIplZw1JxUNMNwBiGqyZKV0ZSj9kHMSUZWFQgIkANLBCBAIJRYhqgJRgxgADzRHBKCmmv7wT38bB//3f/NZKaUfVqvjdT/01qeU7IU/+gQHH9y9ThKEBFRFAAgAgkTw4cNPf/aTn/7w3g/efuvt9957r857OsUkqamoqAAQgQGiKpBK30/z7A6AAIL0cBAEBAoRVRXBE0Gv1RmOSyKQZGYplWw5FU1mogCa17m2Wmt4C0ajh7fmLRgMAhCIqiVLKqqmC8ElNSupiCgAMyu5pFKSaUR4bd6aO3EQjLnO/Wp4/vkbL7/88uvffv2lV1597vr13/6Pz/HER997HiIQwQHxK8FFYCGXACgRTgCqsgDgLercdue7R49ONyfnm9PN+fl2e7Hd78dpnlqdap2bN4JAgIyI2lqEB33RavXmERCRdGCWAJBs7t7C3RlevU11juai6Lr+aH009L2qAghSRdSstba7uNjtxovdRU756ae/cLRe912XS8kpp5zkAAAXHtVbrXU+iKDCBAIgpdR1peuH9XrdD10p2cwgULOcUi6ldP3x8fratavr4+PVqs85q4kI/n9u3n6Ag/t3rosIUAHcvP0ZDt6/ewMLIhwLAUD3uc273bjd7M7Ox/OTeXs+7y+m/a7ud63OdAe4AOEAdJE1XRJVMIgFQTAYJEHwV4KXEMQlAgJIINybE6ZiKZWcNScVDTDcAYhqsmSldGUo/ZBzElGVhUICJADSwQgQCCUWIaoCUYMYAA80RwSgpprUMtREzKws+mG1Ol73Q299SsmgeOE7n+CJ97/3vARURQAIAIJE8OHDT3/2k5/+8N4P3n7r7ffee6/OezrFJKmpqKgAEIEBoiqQSt9P8+wOgACC9HAQBAQKEVUVwRNBr9UZjksikGRmKZVsORVNZqIAmte5tlpreAtGo4e35i0YDAIQiKolSyqqpgvBJTUrqYgoADMruaRSkmlEeG3emjtxEIy5zv1qeP75Gy+//PLr3379pVdefe769dX6KBgAv/THD/HER3dvACB+JbgILOQSACXCCUBVFgC8RZ3b7nz36NHp5uR8c7o5P99uL7b7/TjNU6tTrXPzRhAIkBFRW4vwoC9ard48AiKSDswSAJLN3Vu4O8Ort6nO0VwUXdcfrY+GvldVAEGqiJq11nYXF7vdeLG7yCk//fQXjtbrvutyKTnllJMcAODCo3qrtc4HEVSYQACklLqudP2wXq/7oSslmxkEapZTyqWUrj8+Xl+7dnV9fLxa9TlnNRHBr928/QAH965/47k/+8BMLpETAAIBAfRLtz/BwXvfvQFAAYa3eZ4vdrvt2Xi+2Z9vpovtNG7r7qJOuzrPoIMkDkRFFCknS5qTiQIIgCCDiAgySIIgQRAEQBJAkARBRkSNAGCqalZyVjNVCYARAEzN8qKUritdb6moQEQgqgiAwQAXHgiQCABBAGrJMlQA9YAH6IAaxP7wf/oaDv6v//pBzv3QD6vjVX80lL5LXfryH32Kgw/uXicJUkIUEBUIAIIE8OjhZ3//s5//+Ic/fOutt9599939bqR7LsnMRBQMOslgOAGBNkR192DggEBEAEFngBFBqoiqiqqJAGi1BUMWEACqkmyRS1dyTjllAap7q/N+v3dvRDjoXqu3cPcIAApRmJolMzUzVcElNSupiCiAZKl02VJJpiBqrdG8NQeCRAtfDEdH16/ffOXrr/zD17/90quvPP3ss1/9j87wxEd3rgMCAUkAAUCgkAAXAEQEgEJAgAQgIliQrfm0r9uzi5NHm9PT87PTs4vt7mLcTtM4zXOdp1qn2hroAQKMCHeP8HD3aM0XQScAUTUzVVNVABEMD/eIaLXVqc4MikrXlfV6nXMG6REgVTR3JSLGS/txdyGqV46Ph37IJedSulJyzmZJTQGQDPfml8IvgWJigAJIOeWuDH2/Wq1K3xczTQJRM8s5ldKXvhwfrb/w9LWj9XoYSspJBBCQWIjgczdvP8DB/Ts3RABUADdvf4aD9+/ewIIIx0IBhrd5ni92u+3ZeL7Zn2+mi+00buvuok67Os+ggyQOREUUKSdLmpOJAgiAIIOICDJIgiBBEARAEkCQBEFGRI0AYKpqVnJWM1UJgBEATM3yopSuK11vqahARCCqCIDBABceCJAIAEEAaskyVAD1gAfogBrE1BLEAIWlnPuhH1bHq/5oKH2XuiQqL3znEzzx/veekxAFRAUCgCABPHr42d//7Oc//uEP33rrrXfffXe/G+meSzIzEQWDTjIYTkCgDVHdPRg4IBARQNAZYESQKqKqomoiAFptwZAFBICqJFvk0pWcU05ZgOre6rzf790bEQ661+ot3D0CgEIUpmbJTM1MVXBJzUoqIgogWSpdtlSSKYhaazRvzYEg0cIXw9HR9es3X/n6K//w9W+/9OorTz/7bL8aIhzAl/7kMzxx/851AAFAoJAAFwBEBIBCQIAEICJYkK35tK/bs4uTR5vT0/Oz07OL7e5i3E7TOM1znadap9oa6AECjAh3j/Bw92jNF0EnAFE1M1VTVQARDA/3iGi11anODIpK15X1ep1zBukRIFU0dyUixkv7cXchqleOj4d+yCXnUrpScs5mSU0BkAz35pfCL4FiYoACSDnlrgx9v1qtSt8XM00CUTPLOZXSl74cH62/8PS1o/V6GErKSQQQkFiI4ObtBzi4d/0bz/3Zh2YiAiH3BEAEBKJfuv0pDn75vRsICMDwNk3Tbrc9Ox3PNuP5Zr7Y1t122l/M+13UmeERVBXIgZpYSpYkJTMVNQAMOskIRpAMQAEIAIHg15zR/FIEAaipmaWUTBUiAAJUiCbLKedUUtflUpJlEUBEBRAoGAyQQWe4RzAIBkmIiZmaQYRQhhAKGNT+gz/9Gg7+z//qA81l6IfVejWsj/r1UPry5T/6FAf3794IEKQSCIgKBACxIE8fn7z37r9550c//v5b3//F3//ibHMatZVSLJmKRLg3b7W51whCRUWhKRQBIIgDAhHhrUXz6g2AqiaznLNH7MfRWxMILhHEQs1KKV0upSsiQqDWedpPRKRsUDRvi/CDCEAMampqlsw0mYmomUCTmqhAkMxSLtmyJhMgmod78wj3CHenh6/X65tfvPXKq699+x99+6WXX37q6ae/+s82eOKjOzcgIOkeWCgglxQSIA4UggMhfoVkxDTV/W5/fnbx+NHmbLPdnm+3F+O0H6d5nOa51mnaT+416MFQgUeEe4R7eITHJTIYxEJFRE0/JxKBCEY09zZ7YwTAlFI/DCpSa20Lj2Ta9b2qtlrneZ6mCUApJeVsZl0puZSu60oppqYqEQwGI3AgqipmMFEFYGa55K7ru67LJadkolCI5pxL6brS98PxenX12lPr9VFXckpGwSJIACoLLG7efoCD+3duiACYCdy6/QgHH9y9TggIEggIwPA2TdNutz07Hc824/lmvtjW3XbaX8z7XdSZ4RFUFciBmlhKliQlMxU1AAw6yQhGkAxAAQgAgeDXnNH8UgQBqKmZpZRMFSIAAlSIJssp51RS1+VSkmURQEQFECgYDJBBZ7hHMAgGSYiJmZpBhFCGEAoY1EQMIi2gkjWXoR9W69WwPurXQ+mLqLzwnU/wxAd3nlcCAVGBACAW5Onjk/fe/Tfv/OjH33/r+7/4+1+cbU6jtlKKJVORCPfmrTb3GkGoqCg0hSIABHFAICK8tWhevQFQ1WSWc/aI/Th6awLBJYJYqFkppculdEVECNQ6T/uJiJQNiuZtEX4QAYhBTU3NkpkmMxE1E2hSExUIklnKJVvWZAJE83BvHuEe4e708PV6ffOLt1559bVv/6Nvv/Tyy089/XQ/9O4OwQt/8hmeeP+7z2GhgFxSSIA4UAgOhPgVkhHTVPe7/fnZxeNHm7PNdnu+3V6M036c5nGa51qnaT+516AHQwUeEe4R7uERHpfIYBALFRE1/ZxIBCIY0dzb7I0RAFNK/TCoSK21LTySadf3qtpqned5miYApZSUs5l1peRSuq4rpZiaqkQwGIzAgaiqmMFEFYCZ5ZK7ru+6LpeckolCIZpzLqXrSt8Px+vV1WtPrddHXckpGQWLIAGoyK03HuDg3vVvPP/n91UgAiFHgkEAomK3bj/Ewfvfu4HAJfc6TePFdnd2ut2cjmcn++15Hbd13M37Xa0zvCEIE4XCRNTMklgyU1ETNUAAkiAJMnBJRSCACAARwUFjtNZIYiGSzMQ0aRJTEUCEpIgkSylZSiVZsZKSZQhUAIEKLtGDEeGLcKd7C2eQArUkZqYGMVIDBjFAbv/Pr+Dge//luymXbhjWx8fD8dHq+Kj05ct//BAH9+/eCBCEkoAIAAFALMizzdnHH374k7/6mze//+bP/vZnn3768bSfSi7ZDAAZtbbw2pqTFBO1nHIH08YgiAOBRHhUr22utTGoqilZyYXkuBvdm6hi0SIYHqEQy6krXdeXnDICLeo0TxGRssHQ3MNbcw93j2AQ0ATVbNmSpZRTUjVTBSAQACKaLGmyZElF4AxGa05GhAfB4JUrV7/0lRe+/tpr33z99a+9+LXja9d+85+e4ImP7tyAgKS7kxQRmApERYIkKCIKwYIAiQN3j9b24/78fLc5Pd+cnJ1tduNu3I3jNO1rnWub2zxP81TbHOEkRUAw3ONSIwNAEBGMICIIiIiKqZmpAgrQGREeDI9guIjkUiJinqZaW201mQ3DYGYk3b02JyOZqaqo5pRyudR3XUoJIiCDBKCqppZLLimbFoVgoWrJck6lFMvZDKYmgpRK6UvXdf0wrI+Orj51dbUacknJlIIgGQRgJgsAN28/wMH9OzdEAEwEb90+wcH9u88HhBQQCFxyr9M0Xmx3Z6fbzel4drLfntdxW8fdvN/VOsMbgjBRKExEzSyJJTMVNVEDBCAJkiADl1QEAogAEBEcNEZrjSQWIslMTJMmMRUBREiKSLKUkqVUkhUrKVmGQAUQqOASPRgRvgh3urdwBilQS2JmahAjNWAQAwRqDA1CxFIu3TCsj4+H46PV8VHpi5i+8J1P8MQHd64rCYgAEADEgjzbnH384Yc/+au/efP7b/7sb3/26acfT/up5JLNAJBRawuvrTlJMVHLKXcwbQyCOBBIhEf12uZaG4OqmpKVXEiOu9G9iSoWLYLhEQqxnLrSdX3JKSPQok7zFBEpGwzNPbw193D3CAYBTVDNli1ZSjklVTNVAAIBIKLJkiZLllQEzmC05mREeBAMXrly9UtfeeHrr732zddf/9qLXzu+dq3rirtD8MKffIYn3v/usyRFBKYCUZEgCYqIQrAgQOLA3aO1/bg/P99tTs83J2dnm924G3fjOE37Wufa5jbP0zzVNkc4SREQDPe41MgAEEQEI4gIAiKiYmpmqoACdEaEB8MjGC4iuZSImKep1lZbTWbDMJgZSXevzclIZqoqqjmlXC71XZdSggjIIAGoqqnlkkvKpkUhWKhaspxTKcVyNoOpiSClUvrSdV0/DOujo6tPXV2thlxSMqUgSAYBmMmtNz7Gwb3r33j+z++rQARC7kgGFqKwW298hoP379wAIYHwNo/TtNtuN6fb08fbxw9327M2Xkz7XR3HWid4RRAqqgoVE4OZqqkZRIICERWFqkHEVACIQAQLwa9RhAyPACCqySylpOmSqIgoADJEVAVmpmpJk+VsZhCoCgQKCEgiwsO9+hzNW6veWm1OQMzUzFJSzYBBLCCA/vH/8rs4+N//i7/tSrdar4+vXlkdr/v1qvTly3/yGQ7u372BBQlCAIgABIgFOe52jx8++vlPf/rmm2++8847H/7y/fPzM0uWzCRA0D0YTg8xSTlpKqmUEA1GgAAEEBGSrbrXWtscQRVJaikXUXhtjFAzLJzVq1d3RjLLOfd9n1NSaNDnOteoQW9BIiJ8QQ9fBEGISEopp1zyouSSBRLuIBgEIBBAky0EUAGIAHFJIGrXrn3hN776m1//+mu/9w9+/ytf/c2j9fGX/+ljPPHRnRsQeES4E1QxmCxAkAQIEYFAAAJOgiDbXOdpf7HdnWzOzk7PN6fb7dk4jeM0zXWemzeP1upiX1sLOkgVCTAiGAuHUkUBhCMi3FssCIWoXVJNgEADC5UIn+aZEWYWEdM0zfPcmovK0PcpJfyaiMolAKqac045d6WYGSC4REByyaWUvuu60udUVCwYAFRVzFKylExUTFXNcil93/V9PwzD0dHR8ZXjfuhyNjMlECSDAMxkAeDm7Qc4uH/nhgiAPclbb5zi4P7d5wANCAkQEghv8zhNu+12c7o9fbx9/HC3PWvjxbTf1XGsdYJXBKGiqlAxMZipmppBJCgQUVGoGkRMBYAIRLAQ/BpFyPAIAKKazFJKmi6JiogCIENEVWBmqpY0Wc5mBoGqQKCAgCQiPNyrz9G8teqt1eYExEzNLCXVDBjEAgIoYIR4QDR3pVut18dXr6yO1/16VfoiyV74zid44v6d6yAEgAhAgFiQ4273+OGjn//0p2+++eY777zz4S/fPz8/s2TJTAIE3YPh9BCTlJOmkkoJ0WAECEAAESHZqnuttc0RVJGklnIRhdfGCDXDwlm9enVnJLOcc9/3OSWFBn2uc40a9BYkIsIX9PBFEISIpJRyyiUvSi5ZIOEOgkEAAgE02UIAFYAIEJcEonbt2hd+46u/+fWvv/Z7/+D3v/LV3zxaH6eSPRqAF/7xIzzxy//jGYIqBpMFCJIAISIQCEDASRBkm+s87S+2u5PN2dnp+eZ0uz0bp3GcprnOc/Pm0Vpd7GtrQQepIgFGBGPhUKoogHBEhHuLBaEQtUuqCRBoYKES4dM8M8LMImKapnmeW3NRGfo+pYRfE1G5BEBVc84p564UMwMElwhILrmU0nddV/qciooFA4CqillKlpKJiqmqWS6l77u+74dhODo6Or5y3A9dzmamBIJkEICZ3HrjYxzcu/6N5//8vgqgEsvQAAAgAElEQVREIOQFySAAUbFbbzzCwQd3boBAIFqd9/vd+fl2c3p+8tn5o4fj+abud3Ucp2nX5oltBgkRE4EJoGoGUYgGJEiIJE1qKeekZiICgGSQwSAZQQBUIYggVHJKKedcSi45p6xmKhAIPqeqAlmoJTNRFYEIFCJQIAA6Pdxrm1utbZ5ra95aiKRUUk6WsloRMcCcIOWf/K//Lg7+t//8ndIPR8frq089dXRl3a9WeShf+cePcXD/7g0sCAFBQAQgQCzIedyfbzZ/97Of/4s33/zxj3747t/94uT0JJmpAAQXQYAg1dRyTjnDEkQrnbgkIqYKwD2itrlW0AEkMctZVRgwhZqJKD1aba3V1lwU2XLpu2ymagifvc11nr16NAqDhEcwwt2DDAo0JSspd33f5VK6IqIgGAtv7qwkQ0RFJZkpFICoiGjOlnN59rnnv/ZvvfTK17/+u7/779x84YvdMLzwH36GJz66cwOAM8IdgJiKqIoEF4GFiEAgAAnnpYh5nncXF2eb88ePTzYn55uT7cXFOO/nhXuL8Ah3b7VOtTUyRCiiACOCIEBVMVVAQDSPWqu7h3sQesnMkqpAoapixvBxv49wNYuIOs+1tVqbCkopZiYiEFERM1NViESEiphZSinnrGqioiIA1KwrpXTd0eqoKyVZETGGRwAKVTE1NRVDMksplVK6YeiHYej71dHR8fG674slMxMCQTIIwEwWAG7efoCD+3duiAAYSd56Y4ODj+4+RyggQYBAIFqd9/vd+fl2c3p+8tn5o4fj+abud3Ucp2nX5oltBgkRE4EJoGoGUYgGJEiIJE1qKeekZiICgGSQwSAZQQBUIYggVHJKKedcSi45p6xmKhAIPqeqAlmoJTNRFYEIFCJQIAA6Pdxrm1utbZ5ra95aiKRUUk6WsloRMcCcIIVQBpwwK6Ufjo7XV5966ujKul+t8lDU7IU/eogn7t+5ISAIiAAEiAU5j/vzzebvfvbzf/Hmmz/+0Q/f/btfnJyeJDMVgOAiCBCkmlrOKWdYgmilE5dExFQBuEfUNtcKOoAkZjmrCgOmUDMRpUerrbXamosiWy59l81UDeGzt7nOs1ePRmGQ8AhGuHuQQYGmZCXlru+7XEpXRBQEY+HNnfX/pQvemi27rvuw/8dlzrXW3vvs0w0C6O4DkhbBG5qkJUtERRDxkBe/omE4dsWpKKq4ylVJpcpvechDvkSUh5SYcuSqpCoPic0C8BFMhRc0wItNUyUSpASoL+w+98vea805xxhZZ4MtUQ/+/SLCiZiYVITBAIiJiFOSlPILL9760t1Xvvr3//7v/d7XDz776W4YRMSsgfDZf3KEZ/7q3z4PgISJmIk8Zo4ZEYFAQAQsrrmXUjZXV+dnF8fHJ2cnF2cnl1dX2zKWmVlzN3cza7VOtbUIJwoiBsLdAwEEMwkzQAg081qrmbmZB/iaiCgzgcHMJBJu23F0NxZx91pKba3WxoScs4gQEYiYSESYGUTuzkQioqopJWYhJiYCwCJdzrnrlotll7NKJpJwcwcYzCQsLEwCFVHVnHM3DP0wDH2/WC739lZ9n0VFhALwiPAAIEIv3XuMnfu3X731Jw+YQASKuIwIDwDEJC/dO8bOx+/cCQMQXuu42Wwvzi9PTy6Oj85PDrcXZ3XctO12mjZWRm/Vw5kAIiYCkYMAdlAEmjkzS84p5ZxySgoiN6/Wamu1VTdzd4sAIXBNmFPOueuGrkt916WsOYmIihAIzHDH32BmgAgEgACaARHh5ma1TbXWVkq91lh1sVzmrldJ0ARIBMzggX/2r17Hzv/1z9/rhsVqvV7fvLFa73WLIXX58//sDDsP3r2DHQo8E0BgFlG20+X52c//4mff/va3f/DBB3/+H396dHQoogQEAhGYeQSCmTQliDhgQHgEQEwgKAuLIMKbN2vhjggCsYgwExEzp6RMggg3K7W6WUQwU0qZWZgQEea11jbV0mCYMQgI9zD3iPAgMIvknPuu63KXu5xmLABqs1rqNE5Wq3kASCLMwkwiklLKXV4slp/+zKe/9tu/85Wvfe3uV7/y/K1bovrSP/4Vnnn4zp2IcES4AyBhJgbgiBkAAjERZhGwsPBw327Hi4vzs+PTw8Pj4+Ozy9PLq8ttmUotzcM8HOHmVlvxZg5ngJUBuDsIzCTCIsIgDzKzWmtrrdbq5pgRMwsLMxMxa1Z3n8bRw1UVgLvX1twsIkSViTwCgDCzSEqJidw9PEhYRVJKzEJMTMxMojp0fTf0i8WyS5lJEHB3iwgHCYSFhVg4qSTNqc/DrB+GxbCYLZfDkFlYhECIgEcAYJphdvDGI+w8eOcOEYBNRLx07xw7D999IcAeBFAYgPBax81me3F+eXpycXx0fnK4vTir46Ztt9O0sTJ6qx7OBBAxEYgcBLCDItDMmVlyTinnlFNSELl5tVZbq626mbtbBAiBa8Kccs5dN3Rd6rsuZc1JRFSEQGCGO/4GMwNEIAAE0AyICDc3q22qtbZS6rXGqovlMne9SoImQCJgBg+Yww0WUM3dsFit1+ubN1brvW4xpC5LTr/1jw/xzIN371DgmQACs4iynS7Pz37+Fz/79re//YMPPvjz//jTo6NDESUgEIjAzCMQzKQpQcQBA8IjAGICQVlYBBHevFkLd0QQiEWEmYiYOSVlEkS4WanVzSKCmVLKzMKEiDCvtbaplgbDjEFAuIe5R4QHgVkk59x3XZe73OU0YwFQm9VSp3GyWs0DQBJhFmYSkZRS7vJisfz0Zz79td/+na987Wt3v/qV52/dElUAZg2Ez/6TIzzz0bdeAEDCTAzAETMABGIizCJgYeHhvt2OFxfnZ8enh4fHx8dnl6eXV5fbMpVamod5OMLNrbbizRzOACsDcHcQmEmERYRBHmRmtdbWWq3VzTEjZhYWZiZi1qzuPo2jh6sqAHevrblZRIgqE3kEAGFmkZQSE7l7eJCwiqSUmIWYmJiZRHXo+m7oF4tllzKTIODuFhEOEggLC7FwUkmaU5+HWT8Mi2ExWy6HIbOwCIEQAY8AwEQv3XuEnfu3X731Jw+YQASKuEBEYEYEPbh3jJ2P3r4ND0TUqZSrq8uLi4vT48vT44vjw+nyvExbGzdl3FiZvBV3AwgEJnYgAu5uwc1nwapd16fumooAaLVNs1rGMrXaHBGYERjuISK5S33uumHo+q7LOc00SVIVJSYAMTN3hLsjghBExDQDQIQAPNxqm9VPmFnStLe/PywWoolYA2wOc7jhv/xXr2PnX//R/9f3y8Xeav/mjcV6b1gsdOhf+a8vsfPg3TvYocAzAQRmEWU7XZ6f/+LDD7/zne/84P0PfvIffnz45JBFAEQ4PHAtwhwMUgWTOzUEdkhYmESSMDE44M0szOD4BDETURJRFRYhcLi32tzMwwGwiBITKGDNrHorrbg7BAQiJgbMYwYPgFVEU+q6nDXnLne5G3JPTLXWUsq4GadxqrUigkVUhJiSpK7vl8thb3//c5/73O99/dVXvvrVv/fyyzeeuwmig7ce45kH79z2iPDAjGnGRNjxCABMhE8EEOEzs+12e356enR08vTJ4dHR6eXZ1fZqO461tWpmCPcIdzOr7g0IEJgZswgQaZKkQsxMDJB7WGu11KnW1lq4AyASMLEQiyRV95jKaOFdzkzsCJ+ZRQQxAzAzAEwkIilnJnL38CAmFkkpqQgxqwiLpJSGvu+HYblYiCQ43GHNAh4IgFhYRFQlJUk5567rh2Ho+n7oF4thuVr2fScqIgRCBP4GEWYHbzzCzoN37hABuELEwb0L7Dx89/kAI8iC4IGIOpVydXV5cXFxenx5enxxfDhdnpdpa+OmjBsrk7fibgCBwMQORMDdLbj5LFi16/rUXVMRAK22aVbLWKZWmyMCMwLDPUQkd6nPXTcMXd91OaeZJkmqosQEIGbmjnB3RBCCiJhmAIgQgIdbbbP6CTNLmvb294fFQjQRa4DNYQ43VDM3NAeL9v1ysbfav3ljsd4bFgsd+pTT5//pCZ558O4dCjwTQGAWUbbT5fn5Lz788Dvf+c4P3v/gJ//hx4dPDlkEQITDA9cizMEgVTC5U0Ngh4SFSSQJE4MD3szCDI5PEDMRJRFVYRECh3urzc08HACLKDGBAtbMqrfSirtDQCBiYsA8ZvAAWEU0pa7LWXPucpe7IffEVGstpYybcRqnWisiWERFiClJ6vp+uRz29vc/97nP/d7XX33lq1/9ey+/fOO5myAKhJmB8Nn/4hDPfPStF8E0YyLseAQAJsInAojwmdl2uz0/PT06Onn65PDo6PTy7Gp7tR3H2lo1M4R7hLuZVfcGBAjMjFkEiDRJUiFmJgbIPay1WupUa2st3AEQCZhYiEWSqntMZbTwLmcmdoTPzCKCmAGYGQAmEpGUMxO5e3gQE4uklFSEmFWERVJKQ9/3w7BcLEQSHO6wZgEPBEAsLCKqkpKknHPX9cMwdH0/9IvFsFwt+74TFRECIQJ/46V7j7Bz//art/7kAROIQBHnmEUADJKDN06w89G3XkSEW9RxGjdXV+dnZydHV6cnm/PT6erCpm0bt3XatDJZLXBDhBMC8ICbNUNz8wBIUspd33e5S11HwmE+1bLZbLbjOJXJzDBjERUA1YwZSXPX5ZRz1+U005RzTjmlnFWEiCLC3N2s1eJmADGBmISImIjAICDMzd2stubWmuXc3bx5c1guRBOxBtgc7qjm//Sb38DOn/7hv0u5XyxXy/X+ar0a1qs8DF/7byfsfPzubQYBoMAzAQRmEWU7XZ6f//IXv3zv+9//4Afv/+SH//7J0yciTEAzQwSAcPfaDAFmCDkoQMHExKySRFhERQjXwsJnZnHNMQswc1JVFmYG0Jp5eLgBIDBAhIhwD6tmxWrAiQlEKkxEACKAAIGIOalmVdGUVLuuXy2WLOJm01Q2V5txuy2lRDNWERJi5JyHxXK9Xj9387nPf+kLv//aN75095UX79xZ7q0AOnjrEZ558PZtcweCmEHERPhPo0CYW/j2anN2dnr09Pjx418dH51uzjabzbgdp1pray3cPMK9WaseBgQIzAzA3VU45ZSSMjMRM7EHwr2Z1VJruxbuHgQCCxGzqnp4LSUQueuUBYCHzwAws0dYa+4eEcScUmJmRGBGJCIpJVVNei3Nch76oR+Goe+YpFVrzcLDw0EAsYioiqqkrHmn6/vcdTnnxTCs9pbD0KcuqTAIswjMiPCJgzceYefBO3eIAFwCOHjjAjsP3/0UwBFkBkS4RR2ncXN1dX52dnJ0dXqyOT+dri5s2rZxW6dNK5PVAjdEOCEAD7hZMzQ3D4Akpdz1fZe71HUkHOZTLZvNZjuOU5nMDDMWUQFQzZiRNHddTjl3XU4zTTnnlFPKWUWIKCLM3c1aLW4GEBOISYiIiQgMAsLc3M1qa26tWc7dzZs3h+VCNBFrgM3hjmpuFq16tQBRyv1iuVqu91fr1bBe5WHQLr3yX13imQfv3qHAMwEEZhFlO12en//yF7987/vf/+AH7//kh//+ydMnIkxAM0MEgHD32gwBZgg5KEDBxMSskkRYREUI18LCZ2ZxzTELMHNSVRZmBtCaeXi4ASAwQISIcA+rZsVqwIkJRCpMRAAigACBiDmpZlXRlFS7rl8tliziZtNUNlebcbstpUQzVhESYuSch8VyvV4/d/O5z3/pC7//2je+dPeVF+/cWe6tAAIiwkF46a0neObjt2+BiInwn0aBMLfw7dXm7Oz06Onx48e/Oj463ZxtNptxO0611tZauHmEe7NWPQwIEJgZgLurcMopJWVmImZiD4R7M6ul1nYt3D0IBBYiZlX18FpKIHLXKQsAD58BYGaPsNbcPSKIOaXEzIjAjEhEUkqqmvRamuU89EM/DEPfMUmr1pqFh4eDAGIRURVVSVnzTtf3uetyzothWO0th6FPXVJhEGYRmBHh4I1H2Ll/+9Vbf/KACUSgiHPMAgCB5OCNE+x89K0X3MKa12naXl5cnZ+dHx9enp1O52dlvLJp26ZtnbY+jdaKm3kg4I7whupWmzWzAERzSt0wDKnrUpcZVK1N03R5tZnG7VSKBUQkJdUug7i1AkBVRYTl11S1yylp7vpOkwozAHNvtdZxbNYYxCAIKRMJK7EIExEoZua/prlb7637fuCUhYUgATLAHf/of/t97PzpH/470dT1w7Bardb7y/29vFz87r8w7Hz0zm0hAkCB3xBAIKKVurm8+vgv//L+/fsffPD+D3/wwyePHxFxhLdmYc7CYW7W3ANKDjIPZ2JhFk3CIklVmISZAIRbWLibmbsbAuFOIE0qIgwGEPAIwCPgmAUQiDDzsLDqLeDERMLCwsRgIoCcADCDSYRFRUg4p7xcDKopArXU7WY7bcapTO4uzMRExH3XLVfLmzduPv/CC19+5cuvfeP1L375y+vnPzUMCwAHbz3CMw/evm1uAIiZiUCEZxwBgEH4RICAiHD3cbM5OT45fHr4+OGT45Oz7eV2uxnH7ThNpdbarIVbs2atmjcgiIiZAHiECHd9l5ISkbAwCehauJfarLVaS2tuHqAAETOJiIeXWogo5awiHoEIdwegqgBqra01dyeilJKIeAQTgSipatrRlHJKmnKX+74fur4bBgTKWMwMAQ+AwEzCSbNokpxzSpp3Us5JUz/0q73lMAz9kEWUmUBAAAQifOLgjUfYefDOHSIAlwAO3rjAzsN3PwVQBJnBLax5nabt5cXV+dn58eHl2el0flbGK5u2bdrWaevTaK24mQcC7ghvqG61WTMLQDSn1A3DkLoudZlB1do0TZdXm2ncTqVYQERSUu0yiFsrAFRVRFh+TVW7nJLmru80qTADMPdWax3HZo1BDIKQMpGwEoswEYFiZv5rmrv13rrvB05ZWAgSIAPcUT1q81JaBImmrh+G1Wq13l/u7+XlInX5a//NFs88ePcOBX5DAIGIVurm8urjv/zL+/fvf/DB+z/8wQ+fPH5ExBHemoU5C4e5WXMPKDnIPJyJhVk0CYskVWESZgIQbmHhbmbubgiEO4E0qYgwGEDAIwCPgGMWQCDCzMPCqreAExMJCwsTg4kAcgLADCYRFhUh4ZzycjGopgjUUreb7bQZpzK5uzATExH3XbdcLW/euPn8Cy98+ZUvv/aN17/45S+vn//UMCxwLYAAcPDWr/DMx2/fYiIQ4RlHAGAQPhEgICLcfdxsTo5PDp8ePn745PjkbHu53W7GcTtOU6m1Nmvh1qxZq+YNCCJiJgAeIcJd36WkRCQsTAK6Fu6lNmut1tKamwcoQMRMIuLhpRYiSjmriEcgwt0BqCqAWmtrzd2JKKUkIh7BRCBKqpp2NKWckqbc5b7vh67vhgGBMhYzQ8ADIDCTcNIsmiTnnJLmnZRz0tQP/WpvOQxDP2QRZSYQEACBCAdvPMLO/duv3vnmAwBEoIgLBHYIkIN7x9j56N++0JpHszKOm8vzy7PTs+Oj7dnZdHXetletjK1s67hp02RtcmsR8HD3aG6lWW1WWgVJ7vouD33fd12Xcnag1jKO09V2M02TNQNLN3R9P8xYeCrFw5kYCI9PODOpakq567qck4qACOa11Wm7raUyQEwsLMyaJKmoqKgwM4gwC0SARFLKmnNKWTQzC7GCJcD3/tffw86f/tGfAUg5D4vVYr3au/HcsFp8/b8n7Hz0rVskJEQI/F2BiDCzUv/644/vv//++/ffu//eew8fPIS5ubVSgWARAG4GgIQdMRYzhCYV1ZQ0cWIRZmLCzC3C3N2ambuFRYTTjFlZmEDggGMnAtdiBoeHu4c3t0CQEAkrCwkLM4EIIPwtAgNQkSxZkgqzmU/jVKbSajVzFZ4RU5f79Xr93Keeu33rxS/dvfsH33j981/84vLmjb7vATp46xGeefD2bXMHQMJM5AgADHLEDAARAeDAjILCwyPGq83JyfHTp4ePHz05PTkrY9luxs1mO43jNJXWarVqrdZamhngRMyEmQdUpBtySspEzMKssgMg3GtrpdRam7tFBAigaxbeWgWQcyYiM/MdZk45M1Ftzdo1EOWUiBkAE4mIiqacUs5JU8op59zlnLtZ3+WMwDQVM4MDRCAWJklJVVLWLidNWVVyyqyiIrkf9lbLxXLohz6nRMIEAgIMphlmB288ws7Dd+/g2hUCB/fOsfPw3ecjyANhaM2jWRnHzeX55dnp2fHR9uxsujpv26tWxla2ddy0abI2ubUIeLh7NLfSrDYrrYIkd32Xh77vu65LOTtQaxnH6Wq7mabJmoGlG7q+H2YsPJXi4UwMhMcnnJlUNaXcdV3OSUVABPPa6rTd1lIZICYWFmZNklRUVFSYGUSYBSJAIillzTmlLJqZhVjBEuBwlGZTqdUCQMp5WKwW69XejeeG1UK77nf+ecEzD9+9g8DfFYgIMyv1rz/++P77779//73777338MFDmJtbKxUIFgHgZgBI2BFjMUNoUlFNSRMnFmEmJszcIszdrZm5W1hEOM2YlYUJBA44diJwLWZweLh7eHMLBAmRsLKQsDATiADC3yIwABXJkiWpMJv5NE5lKq1WM1fhGTF1uV+v18996rnbt1780t27f/CN1z//xS8ub97o+x4gMIAAcPDmYzzz8Tu3mcgRABjkiBkAIgLAgRkFhYdHjFebk5Pjp08PHz96cnpyVsay3YybzXYax2kqrdVq1VqttTQzwImYCTMPqEg35JSUiZiFWWUHQLjX1kqptTZ3iwgQQNcsvLUKIOdMRGbmO8yccmai2pq1ayDKKREzACYSERVNOaWck6aUU865yzl3s77LGYFpKmYGB4hALEySkqqkrF1OmrKq5JRZRUVyP+ytlovl0A99TomECQQEGEz00r1H2Ll/+9U733xAhBlFXCFwLQgkB/cOsfPRv3mhVbdap3GzOT+/PDu9PDnenJ2UzUXdbqyMrWzrtG1ltFbNzMPcEfAWUc2auZmBWDSnlPvcS1Jm8fBpKtOsFHMj1tz3i9VisVguFgMxT6W4GQhuEWHNrLUGuBBL0i7llBOLCDM8aivbqyurlYmEr5GwCrNoyimnaywiLA64AyDSa6w5ibIkImFRB7/xx7+DnX/9R38WgIhoPyxWq9XN51Z7q9f+ZcLOR9+6JUIzBP5WYCfgDrPHjx796Ic/uv/+/fe+992/+uijMhWr1cwQQUQAAoFrZOFTbQYkUUmqokmEVYgYs/DwCHc3cyDcIuDuBBAzEzMRAYFAwOEIXCNcCwQi3FuYw4kITMLCxCyiTMwixAQKhJmHm5sBYBFlYRYA3qw1q7WFuwgziIS7rttbr59/7rnbdw5euXv3tddff/kLX1ju7/dDD9DBW4/wzIO3b5kHGETMBEfgGiEQCESAiECMaxQUHma+udocHx0dPj361ePHZ6fn01Sm7bjZjtM4TuM01dJaba3WWtwtAoATMYAIZ+aUVUWIWVWTZk0pqYIIEc3MmjVzhFsEAI9AuLs1NwDC4girrbkhICKakjCZ+ScACDMxMxEJq2rSpCkl1bTT5dzlruu7lLImRaCUZq26BwIAsXBKWZOknJOKppxUJOUkwiq565aLxWK5GBZD1+WUlJiBAIOJCNcO7j3CzsN37+DaFoGDe6fYefjOCwFyQ3i06lbrNG425+eXZ6eXJ8ebs5OyuajbjZWxlW2dtq2M1qqZeZg7At4iqlkzNzMQi+aUcp97ScosHj5NZZqVYm7Emvt+sVosFsvFYiDmqRQ3A8EtIqyZtdYAF2JJ2qWccmIRYYZHbWV7dWW1MpHwNRJWYRZNOeV0jUWExQF3AER6jTUnUZZEJCzqYDcUs6mU1iwAEdF+WKxWq5vPrfZW3TD87r9oeObhu3cQ+FuBnYA7zB4/evSjH/7o/vv33/ved//qo4/KVKxWM0MEEQEIBK6RhU+1GZBEJamKJhFWIWLMwsMj3N3MgXCLgLsTQMxMzEQEBAIBhyNwjXAtEIhwb2EOJyIwCQsTs4gyMYsQEygQZh5ubgaARZSFWQB4s9as1hbuIswgEu66bm+9fv65527fOXjl7t3XXn/95S98Ybm/3w89QGAEAsBLbz7GMx+/c5sJjsA1QiAQiAARgRjXKCg8zHxztTk+Ojp8evSrx4/PTs+nqUzbcbMdp3GcxmmqpbXaWq21uFsEACdiABHOzCmrihCzqibNmlJSBREimpk1a+YItwgAHoFwd2tuAITFEVZbc0NARDQlYTLzTwAQZmJmIhJW1aRJU0qqaafLuctd13cpZU2KQCnNWnUPBABi4ZSyJkk5JxVNOalIykmEVXLXLReLxXIxLIauyykpMQMBBhO9dO8Rdu7ffvXONx8QYUbhI0C4Rgg6ePMJdj76N7eiWpmm6ery8vzs8uz46vRke3E6bi7quIlxbHVbx21rxayam0d4IOAGBDFAILaANwMgoiA2s1rrdpxqMwAp59VytdrfW6/Xw3KRuw5AmabmxsQA3N3cWmvuFhFCxMIqIqrCLIQylavLc2stqYoIgcAEhIio5tx3wzB0uWPWADUzJ06qLAksYBESIgmQRdz749/Bzp/+0Z/hmkjSbrlc3bixWq//8/9xiZ2P377NRIRnAtcc1xhwh9nTp0/+/Kc//dGPfvi97373lx9+eHF+WepI4AhvzQKBa9HM3Nw8HCTMLKKirKIiRBQRPjODBwASEhYH3AwerMJEBARgbmERCARoxkTMDDjgbu6tmQMemBEzq0rS1OWsoqoSgdpKmVqpJcx5RgSACEyCCGvmcASYiIW7Li+Xq5s3P3XnpZfufuXua3/w+stf+ML65o1+GIjo4K3HeObjt18EASAmOBARQBARQBFAAAgCMRGBEDDzKHZ5efX06eHRk6dPnj49Pzsbx3E7TlOZrpWpllJrba3V1iI8IgCPCADmDkQEiJBUc85dP3Q5a0pEFNdA15iJAZibm9U2+UIAACAASURBVNVW3A1EiGjmZq3VZh5EEBYWmmFGJCyYhQPEwiqack4zVRZh5pTS0F/rui7lzGAPtFqsWq3VzDyCSVJWSSmnLKJJRVLKOSfNucu5y13K3aJfrBbD0A9Dp0nwGwhx8OZj7Dx89zZmUQE6uHeInYdv3wqQO8IjqpVpmq4uL8/PLs+Or05Pthen4+aijpsYx1a3ddy2VsyquXmEBwJuQBADBGILeDMAIgpiM6u1bsepNgOQcl4tV6v9vfV6PSwXuesAlGlqbkwMwN3NrbXmbhEhRCysIqIqzEIoU7m6PLfWkqqIEAhMQIiIas59NwxDlztmDVAzc+KkypLAAhYhIZIAWURrXpuV2poZrokk7ZbL1Y0bq/V6tbf++n9neObhO3fwicA1xzUG3GH29OmTP//pT3/0ox9+77vf/eWHH16cX5Y6EjjCW7NA4Fo0Mzc3DwcJM4uoKKuoCBFFhM/M4AGAhITFATeDB6swEQEBmFtYBAIBmjERMwMOuJt7a+aAB2bEzKqSNHU5q6iqRKC2UqZWaglznhEBIAKTIMKaORwBJmLhrsvL5ermzU/deemlu1+5+9ofvP7yF76wvnmjHwYiCoZ7APGZt57gmQfv3nIgIoAgIoAigAAQBGIiAiFg5lHs8vLq6dPDoydPnzx9en52No7jdpymMl0rUy2l1tpaq61FeEQAHhEAzB2ICBAhqeacu37octaUiCiuga4xEwMwNzerrbgbiBDRzM1aq808iCAsLDTDjEhYMAsHiIVVNOWcZqoswswppaG/1nVdypnBHmi1WLVaq5l5BJOkrJJSTllEk4qklHNOmnOXc5e7lLtFv1gthqEfhk6T4JmX3nyEnfu3Xz345gNQAKDwimsEEAIHbz7Gzkf/z61Wat1ux6uLi7PTy7OTzdnpuDmbNhdt3FgZvYx1GpvVcHM3AxzuAJjBwiws4h7TNFkxgJvbVEotZSoVxF2XV6vV+sbNvf31crnMuSOhiKi1RgQzA3B3c2u1ttbCDQAxKYsmTSLCZK1eXly0WoSZiGdEzMIiqeu73HV936fUs0iQmDuINWViCQiuEcABNPN7f/zb2PnTP/ozBEAClrQY9vb3l/v7//B/WmPn42/dZhAxQLgWgOM3BNyOjw4//PnPf/zjH33vO9/98MOfH5+clHEkQkSUWsMdICBqa+ERIIAAEDOLqAiLMK5ZRDQLBIFISFkANLewICHsRCDcI8LcARCImZgYTESIQPPmZoBHYEbEKpKTdl2vqimlCC+1lWmcxtKsMQg7DNKkTNTMsCPMLJJTXi0Xz7/w4sFnPn337ldf/f3XPvf5l9f7N/qhJ6GDNx/jmY/feZEBEHlEIBCBGV2LIMwMBDATgeBoZnVbzs8vDn/15OnTo8Ojpxfn59vtONWp1jKWUqZpqqWVUltzc4/wsAiPCATC3SLCzQMikpJ2XZ+TiioTAWBikaRJRQSAmbVaa6sezszu3mo1s9aMCKoqIsxMfE1FUkogcndE8Ewkp5xzElVmJiIV7fqu7/uhH1QVxN7MSi2lTFMxa2ZOTKpJUk4pqyRRSUm7vNP1qct9l4dhWKwWi+UwLLqUFAQEwjEjjoM3H2Pn4bu3MIsAcHDvCXYevn07nDwiLFqpdbsdry4uzk4vz042Z6fj5mzaXLRxY2X0MtZpbFbDzd0McLgDYAYLs7CIe0zTZMUAbm5TKbWUqVQQd11erVbrGzf39tfL5TLnjoQiotYaEcwMwN3NrdXaWgs3AMSkLJo0iQiTtXp5cdFqEWYinhExC4ukru9y1/V9n1LPIkFi7iDWlIklILhGAAfQzKdqtbbWqnkgABKwpMWwt7+/3N9fr/f/s/8h8MzDt++AcC0Ax28IuB0fHX7485//+Mc/+t53vvvhhz8/Pjkp40iEiCi1hjtAQNTWwiNAAAEgZhZRERZhXLOIaBYIApGQsgBobmFBQtiJQLhHhLkDIBAzMTGYiBCB5s3NAI/AjIhVJCftul5VU0oRXmor0ziNpVljEHYYpEmZqJlhR5hZJKe8Wi6ef+HFg898+u7dr776+6997vMvr/dv9ENPQgG4eyA++9ZTPPPxOy8GAhGY0bUIwsxAADMRCI5mVrfl/Pzi8FdPnj49Ojx6enF+vt2OU51qLWMpZZqmWloptTU39wgPi/CIQCDcLSLcPCAiKWnX9TmpqDIRACYWSZpURACYWau1turhzOzurVYza82IoKoiwszE11QkpQQid0cEz0RyyjknUWVmIlLRru/6vh/6QVVB7M2s1FLKNBWzZubEpJok5ZSyShKVlLTLO12futx3eRiGxWqxWA7DoktJQUAgHC+99Qg792+/evDNvwYFAAo3/BoBOLj3CDt/+X8/X6dp2mw2lxeb89Or87PN5dl0dV63F23aWJm8jK1NZhXhhgDgBBA5C4uAVUQivE6lTrWUNpUyTqU2A9D33Xq9v97fX9/Y7xcDs4b7WEu4g0iYiSgizN2tTaW0VsMMCJmp9Ckl1aTibuNmU8extIZwEOech8WyHxb9MKgm4pkQK5FQSirCoiBxMAIBmMPNq8eb/8vXsPN//OG/AxhEJJKG5Wq9v9rb/4f/8xo7f/X/3iImISLGrzn+rjg+OvrFhz/7yY9//P3vf/dnf/Gzk5OT7bglwM2mqUQ4i4SHmzlmDFAgABImFlG5hh03C/waM4d7c3NzMw8EgIggIgBhHojwAEBMxKwiIFh4uBMDoBmDWCWJqKiKkEhE1FpbLeNY3AwIOGbEnERwLQBKSVlERfq+W+2tX3zx1ssvv/ylr3zlH/zu1z/zW59drda5y2A6ePMRnnnwzosg8pg53AMBEIuAGAgEIYgdxARHBOpUL6+uzo5PD3/19PDp06OTo8vLy6mU2kqtZarTNJWpTKVUa9bMwi3cLWaOCHfHjBjh5gFAmEDMTMJMIEkpz5KKKBC1tmYWbthx92YWHgBUpe+HlFNOSVRFRFW7lFkFswg3B1NKSUVTTsIMIhUR1S7nLncpJ4KY2bjdTmOpZaq1NjOAUlLVLqUsSUUkp1n+RD8My9VisVwuV4vF0HeLrCogIBCOGXEcvPkYOw/fvYVZEICDe4+x8+BbdxBhFt6sTtO02WwuLzbnp1fnZ5vLs+nqvG4v2rSxMnkZW5vMKsINAcAJIHIWFgGriER4nUqdailtKmWcSm0GoO+79Xp/vb+/vrHfLwZmDfexlnAHkTATUUSYu1ubSmmthhkQMlPpU0qqScXdxs2mjmNpDeEgzjkPi2U/LPphUE3EMyFWIqGUVIRFQeJgBAIwh5tXjzKrVktxd4BBRCJpWK7W+6u9/dX+3mv/kvDMg2/dIcavOf6uOD46+sWHP/vJj3/8/e9/92d/8bOTk5PtuCXAzaapRDiLhIebOWYMUCAAEiYWUbmGHTcL/Bozh3tzc3MzDwSAiCAiAGEeiPAAQEzErCIgWHi4EwOgGYNYJYmoqIqQSETUWlst41jcDAg4ZsScRHAtAEpJWURF+r5b7a1ffPHWyy+//KWvfOUf/O7XP/Nbn12t1rnLYAqEewTis289wTMfvf083AMBEIuAGAgEIYgdxARHBOpUL6+uzo5PD3/19PDp06OTo8vLy6mU2kqtZarTNJWpTKVUa9bMwi3cLWaOCHfHjBjh5gFAmEDMTMJMIEkpz5KKKBC1tmYWbthx92YWHgBUpe+HlFNOSVRFRFW7lFkFswg3B1NKSUVTTsIMIhUR1S7nLncpJ4KY2bjdTmOpZaq1NjOAUlLVLqUsSUUkp1n+RD8My9VisVwuV4vF0HeLrCogIBCOl956hJ37t189+N8/xg6FO37Dwb1H2Pnw/7xZpnG8utpeXG4uzzYXZ9uLs7q9qOOVjxtrU6tTtMmihTsAZzhRgCAMVmJhEURYa7XUaZymqUxT8YCq9IvVjRs31vvrxd4qaTb3Uupm3JpZUmVhYQbBI8KsltqsuTUgmFlVckpJNSkjwmsZx2mz3bTaQOj7fr2+sVzuDculqjYPgABm1dT1Sf9/suDtR9Pryg/zbx32fg/fV0V2kyLZ1eoROWNrhuJB0gzHMcYxfBXEuRj20JNgDPhvyARBcpkYAQIkgO+SwIJj2TcOEgyMwATFIAiCAAFyaQsaUBIpnoaimupDdXVVd1V9p3fvvdbKW5/UgQI/TyYRgCwQDvcwRzWrrd7+797E3vf/wf+DAFiEJY/DePjsweEz/95/8Sz2vvyXL7IQMTFAjCuO3xAAzk5Pv/j8sw9/8uN/88N//cVnn589frxer4GwWkutbs4iANwMIBIByMyxpyKaVESImUCBCHcAEQHA3ZuZm7XazM0jmIiZCRQIdzeziADAIkmUhANBDCEmJmJmImYWEmYSEQI8orVam5WpuBn2CESAMAEwd2HOOYuoCPd9d3h4+NJLR3/9m7/76uuvvfGd7xzdujWOC80JwNHt+3jq7g9eAJGHR3i4AyBmImZiRyCIgwAiIBzeYrfdXZyfn52ePXxwcnr66MmTx+vN2lotrZZWSi3TNNVSay21mZu5m/ksEG4eEQ5ARSKi1Gq1eXh4ACAmEKekXddnVUkJgNVqMXNEuHszs9oAsEqX82Kx7Psu5ZxSUtWUUpeyJJ0RkZkxSJLOUkrMTETMLCIp5RkzEXEtZbvelt00ldJq83AiTrOcuzxIUpWUkqaUZymlYRgWh8uD5bhYjN3Yd31SFRAQCMeMOI5uP8DevfdfxCwYwNHb97F3990b7hHVa61l2u3W6+3larM631yeby/P6/ay7ta+21ibWp2iTRYt3AE4w4kCBGGwEguLIMJaq6VOu2mayjQVD6hKPy6fffbZw2cOx4Nl0mzupdTNbmtmSZWFhRkEjwizWmqz5taAYGZVySkl1aSMCK9lt5s2202rDYS+7w8Pn10sDobFQlWbB0AAs2rq+qSZRACyQDjcwxzVrLY6Ta2UUmttHgiARVjyOIyHzx4cPrM4PPyjP2c89dW7NxggxhXHbwgAZ6enX3z+2Yc/+fG/+eG//uKzz88eP16v10BYraVWN2cRAG4GEIkAZObYUxFNKiLETKBAhDuAiADg7s3MzVpt5uYRTMTMBAqEu5tZRABgkSRKwoEghhATEzEzETMLCTOJCAEe0VqtzcpU3Ax7BCJAmACYuzDnnEVUhPu+Ozw8fOmlo7/+zd999fXX3vjOd45u3RrHheYEIBCOAOLW7Yd46s57z4U7AGImYiZ2BII4CCACwuEtdtvdxfn52enZwwcnp6ePnjx5vN6srdXSamml1DJNUy211lKbuZm7mc8C4eYR4QBUJCJKrVabh4cHAGICcUradX1WlZQAWK0WM0eEuzczqw0Aq3Q5LxbLvu9SziklVU0pdSlL0hkRmRmDJOkspcTMRMTMIpJSnjETEddStutt2U1TKa02DyfiNMu5y4MkVUkpaUp5llIahmFxuDxYjovF2I191ydVAQGBcNx85z72fvjSW0ff/wp7FGaYEWHv6O372PvsXzwzbbfTer1drVaX57vV+bRale2lTRsvW6tTtKlZCWuAO+CAA84IYjCDhIhn8DCzOtXWajUDScpp6IflwcE4jtr3zNSKlVp3u6mGMbGoaFIVZmIgwr2ZhTUPZyJhFmEVVmEmIqBM5fLycpomeKShe+aZZw8ODsdxqaItAoEgEk1dN0hS5hRAc7i5ebRm1Wwq9U/++zex90///v+NGQupdMO4PLy2OHzm3/8vr2Hvy3/5IjMRExOI8WuOvQAC7mePHn366acffviTH/3oRz//4ouz07PtdmOtmlkpxdyFGYC5A6SSAmhmEQGAiVPWpElERRhAhJtHuLuZRXizaq2UYrUFgplVlZkBNLNWq7kBUFZJqiIkxMIqwswqQizEIGIBYc/M3G3WqkU4gUCEXzF3M3Mnpj53ogzirusOlgc3b9187Vuvv/bGt9/47neOvn4z9b2qgnD09n08dff9F4GwiDAHgphBVxjkCARxEIFAMAufbLPanJ6dnp48enh88vj07Mnl+W67aTar1epUyzRNre61FuYWEW7ubh4RHh4gqIi5l1JabbXV1lq4ewQAEem6Lu8RsZu5WQDNrLXaaiu1AFDVcRiWy+UwLvIsKTOnWc5d1w3DkEQxY0qaUlIWYWYAwpKSiiYVAdDMym63XW93u6nV6h4AadKh73Puu2HIOSfNmjRdyaLa9/1iMSyWi8VyHMYudUlVQEAgHDPiOLr9AHv33n8RMycAR7cfYO/uuzfcw6uVUqbtdlqvt6vV6vJ8tzqfVquyvbRp42VrdYo2NSthDXAHHHDAGUEMZpAQ8QweZlan2lqtZiBJOQ39sDw4GMdR+56ZWrFS62431TAmFhVNqsJMDES4N7Ow5uFMJMwirMIqzEQElKlcXl5O0wSPNHTPPPPswcHhOC5VtEUgEESiqesGScqcAmgONzeP1qyaTaVOUylTKbWZGWYspNIN4/Lw2uLwmYNnnvmjP2c89dW/usEEYvyaYy+AgPvZo0effvrphx/+5Ec/+tHPv/ji7PRsu91Yq2ZWSjF3YQZg7gCppACaWUQAYOKUNWkSUREGEOHmEe5uZhHerForpVhtgWBmVWVmAM2s1WpuAJRVkqoICbGwijCzihALMYhYQNgzM3ebtWoRTiAQ4VfM3czcianPnSiDuOu6g+XBzVs3X/vW66+98e03vvudo6/fTH2vqiAEwiMA3Lp9jKd+8e5zQBAz6AqDHIEgDiIQCGbhk21Wm9Oz09OTRw+PTx6fnj25PN9tN81mtVqdapmmqdW91sLcIsLN3c0jwsMDBBUx91JKq6222loLd48AICJd1+U9InYzNwugmbVWW22lFgCqOg7DcrkcxkWeJWXmNMu567phGJIoZkxJU0rKIswMQFhSUtGkIgCaWdnttuvtbje1Wt0DIE069H3OfTcMOeekWZOmK1lU+75fLIbFcrFYjsPYpS6pCggIhOPmO/ex98OX3jr6/lfYozDDjAh7R2/fx95n/+KZabvdrte7y9X68nxzeb5bn7ft2nabaDurU1hxrwgDwhEBMsAQDgSxEzMxgVUIhmYtfAZWSTmn3A39oDkRC4Dmbma1mbsbIMJdzilpFiUhRJibW0MEACJSJiYSIWFRTa2Wy8vVNO2smSYdF8txPBjHQVMGCMQgEk2SsqoSS4Dd3Sxas9qstFpK+5P/4dvY+yd/9n8BBGJWzf3i4JlnF89c+7v/8Br27vyvL4KImBggxq859iLcrdZHDx9++sknP/3oJz/54Me/+PLnZ4+f7Lab2qq1Vms1dwYBsHCARNQdbhYIAARKOYlq0pkQcYSbh5s1M3cP91pqqaXWFnBh0ZRUhEDmVktt3iLAxCmpiDATJ0kzVWZhYWECQI5ZABHRmkV4Mwt3AjERM0d4axZmzUxFur5PooHouu7g4ODWb/3Wm9/+zmtvvPmtN9548cZLkrOIADi6fR9P3X3/RSAsAhGY0RUGAfAIgDiIQCBY8zq19cXqZHZ88vD45Mnjs9V6NU3b2lqzVlutrdRaS22tlmYW5hYzd49w84C7ASCi8KillFbNrLVWa7NZq8yiKfVdl3NmEczC3aO2WkuZpqlMNRAp6TAMy4PDYRhSUmUhZhZJSYe+74eh6zoVSZpSl1VUhAECoCop56QKYjebpt1uN02bXSk1IoRZVVPuxnEchnHoF6nPSXNKqppENWnKXe77brEcFouxG/uuT6oCAgLhmBHH0e0H2Lv3/ouYOQE4uv0Ae/feuxEWrVgpZdput+v17nK1vjzfXJ7v1udtu7bdJtrO6hRW3CvCgHBEgAwwhANB7MRMTGAVgqFZC5+BVVLOKXdDP2hOxAKguZtZbebuBohwl3NKmkVJCBHm5tYQAYCIlImJREhYVFOr5fJyNU07a6ZJx8VyHA/GcdCUAQIxiESTpKyqxBJgdzeL1qw2K62W0nbbMtuV4mEAgZhVc784eObZxTPXDp995o/+nPHUV+/eYIAYv+bYi3C3Wh89fPjpJ5/89KOf/OSDH//iy5+fPX6y225qq9ZardXcGQTAwgESUXe4WSAAECjlJKpJZ0LEEW4ebtbM3D3ca6mlllpbwIVFU1IRAplbLbV5iwATp6QiwkycJM1UmYWFhQkAOWYBRERrFuHNLNwJxETMHOGtWZg1MxXp+j6JBqLruoODg1u/9Vtvfvs7r73x5rfeeOPFGy9JziICIBCOAHDr9jGeuvPe85jRFQYB8AiAOIhAIFjzOrX1xepkdnzy8PjkyeOz1Xo1TdvaWrNWW62t1FpLba2WZhbmFjN3j3DzgLsBIKLwqKWUVs2stVZrs1mrzKIp9V2Xc2YRzMLdo7ZaS5mmqUw1ECnpMAzLg8NhGFJSZSFmFklJh77vh6HrOhVJmlKXVVSEAQKgKinnpApiN5um3W43TZtdKTUihFlVU+7GcRyGcegXqc9Jc0qqmkQ1acpd7vtusRwWi7Eb+65PqgICAuG4+c597P3wpbeOvv8V9ihqw4wJRACO3r6Pvb/6n66V3W63Xm8uV6uLs/Xl+e7yvGwubbexuoMXWEM0wEGBGVNzNLgFDOQgJiKQihAIEQBIZqqaVJVYQOQIgEBELMSMiKk1Jur7rstdyqrCmPmsuWPGAAuEiGcinWYP282mqZYSESSSUtd3Q+5ySlk0kwixMEAsxAKi5uFmrVltVmqdSvt7//j3sfe9//D/xIxnuVuMy8NrB9eu/91/eA17d999CSAAxPg1x1PRWp0224cPHnz88Uc/+2j24Z07dy4uLne7bavFzGptER4ejnBzXBEA5uYRAJhIVJNqSklEiDncI6KZeTMPN3errbbqV0KYU07CAqZwb2Zu5u4AmJlmwimJimrSlBKLCBOB8SvuAUREa+ZmEQ6AiYkJgDebuXnOaTEuNGsEutwdHh6+8srL3/39t159/fVX/trvXH/uec5JSAK4+c59PHXv/ZcC4YgZACJiEACPGWYCJiIArXnd1YsnF8fHxyfHDx8dn5yfn293m2naTaXUmddmrdVaanUz83A3BMLdYubugXDzcLvi4e6B8GZWSpmmabfbhQeL5JT7oUuaRIRwpZltN9vdtN1sd2EuSYe+WyyXfdczE4jDjJhUk6h0XTf0s2EY+n4YckqYEQFIot3QJ1UAUymb9XraTa00BFiky7nrhnEcl8uDxXLZD0PXdapJVJWTqIiKJu1y7sd+sRzHoe/GrCogRAAe2Lv5zgPs3Xv/RcwaZkfvHGPv3ns34GjV6lTLbrdbrzeXq9XF2fryfHd5XjaXtttY3cELrCEa4KDAjKk5GtwCBnIQExFIRQiECAAkM1VNqkosIHIEQCAiFmJGxNQaE/V91+UuZVVhzHzW3DFjgAVCxDORTrOH7WbTVEuJCBJJqeu7IXc5pSyaSYRYGCAWYgFR83Cz1qw2K7VOpZVd2ZUrzRwznuVuMS4Prx1cu/7stWf/5n9MeOruuzeI8WuOp6K1Om22Dx88+Pjjj3720ezDO3fuXFxc7nbbVouZ1doiPDwc4ea4IgDMzSMAMJGoJtWUkogQc7hHRDPzZh5u7lZbbdWvhDCnnIQFTOHezNzM3QEwM82EUxIV1aQpJRYRJgLjV9wDiIjWzM0iHAATExMAbzZz85zTYlxo1gh0uTs8PHzllZe/+/tvvfr666/8td+5/tzznJOQBGbhHABu3T7GU3d+8DUARMQgAB4zzARMRABa87qrF08ujo+PT44fPjo+OT8/3+4207SbSqkzr81aq7XU6mbm4W4IhLvFzN0D4ebhdsXD3QPhzayUMk3TbrcLDxbJKfdDlzSJCOFKM9tutrtpu9nuwlySDn23WC77rmcmEIcZMakmUem6buhnwzD0/TDklDAjApBEu6FPqgCmUjbr9bSbWmkIsEiXc9cN4zgulweL5bIfhq7rVJOoKidRERVN2uXcj/1iOY5D341ZVUCIADxuvvMAez986a2j73+FPYpaAQITiAAcvX0fe1/8L8+V3TSt16vzi9X52friyeb8cdlclO3Kyw7RGMZwIqgQhMHsQPVwRANFAEQEEhAhYAEmVmVJKamIgsiA8AgiFlFNSZMDpRQw9V2Xc+5UWRmBCJsBDkCIRUiYmSQlySkToVabSpnKrk6teQNYU+q6oe8HzVlVATazABELiMOt2ZXSrFYrU/nTf/KH2Pve3/s/MGOGpH4cF4fXDq9d/w/+q+exd+/dG2D8/zieilrL5uLy7t27H3304Scf/+zTTz6+d+/u5eVqu922Vt3M3N0cCDe38PAIUDgsPDwAiPBMRDWpihAQgLm5uZm7mbs3swgPD2IiYhVhZhKOCHi4ezOLcFwJIpIkIppzUlURISYhIYBwJYCIaM3cLMIBMDGLAIhW3SM8UpeWy2VOXYR3XT48PHzlld/+/T/8w1e/9drXX3752vXroomIA7j5zn08de/9G4FwxAwIImIQAA9EOJyEmYgAtGp1U548Ob93/8HJ8fGjh6eri4vttJvKbpqm2kq1atZas9pqeLibByI8IhAId4sIt1mp1cyYGAABZraddtN2Wq/X1ZowpZT6YehyZhYVIRE322y22+16vdm6mah0Xb9cjClnJvZwM4sIFQERgKxpWIyLcbFYjDlnZgYRgJRS13WqCmCapvV6XadKAWFJOQ/DsFwsF4vlweHBuFgO/ZByVlUWEUqiwiIpa5dTP/aL5TgOfTd2qgJCBNwDAAM333mAvXvvv4hZC4CO3jnG3r33bsBh5rW0spum9Xp1frE6P1tfPNmcPy6bi7JdedkhGsMYTgQVgjCYHagejmigCICIQAIiBCzAxKosKSUVURAZEB5BxCKqKWlyoJQCpr7rcs6dKisjEGEzwAEIsQgJM5OkJDllItRqUylT2dWpNW8Aa0pdN/T9oDmrKsBmFiBiAXG4NbtSmtVqZSpTKdNUplLdHDNmSOrHcXF47fDa9WeuX/+jam7etwAAIABJREFUP2c8de+9G/j/OJ6KWsvm4vLu3bsfffThJx//7NNPPr537+7l5Wq73bZW3czc3RwIN7fw8AhQOCw8PACI8ExENamKEBCAubm5mbuZuzezCA8PYiJiFWFmEo4IeLh7M4twXAkikiQimnNSVREhJiEhgHAlgIhozdwswgEwMYsAiFbdIzxSl5bLZU5dhHddPjw8fOWV3/79P/zDV7/12tdffvna9euiiYgDs3AOALduH+OpOz94AQgiYhAAD0Q4nISZiAC0anVTnjw5v3f/wcnx8aOHp6uLi+20m8pumqbaSrVq1lqz2mp4uJsHIjwiEAh3iwi3WanVzJgYAAFmtp1203Zar9fVmjCllPph6HJmFhUhETfbbLbb7Xq92bqZqHRdv1yMKWcm9nAziwgVARGArGlYjItxsViMOWdmBhGAlFLXdaoKYJqm9Xpdp0oBYUk5D8OwXCwXi+XB4cG4WA79kHJWVRYRSqLCIilrl1M/9ovlOA59N3aqAkIE3OPWOw+w98OX3jr6/lfYo7AKEIgAAnD09n3s/eIvvlZ20269vry4uDg7WT8+W1+cby4fT5sLn7YMZ44kSMopJ1ZllWAOwAEDBQig8LDW3Jo3Cw8Ii6acc0qZVYklADALC+ssMaGaAZRzUhEWIsDNzFu4ARAWUckqWWeJRZIqMwNozXZTmXbb7a40M2JKKY/jInWdSgJQqpk7iAGEezMzDzOrxUqr/9H/+O9g73vv/O+BADFpSv04Hlw7vH79j//rF7F3770b+Lc5fqVMu9XF+Vd3fvnhT3/88cc/++zzzx7cv7derbfbrbuFWQAR4RFuDkQzdwv3wG9gZhIWZiYGEIiZubtZM3Oz8GBhgIhJiImJQCRMIGZyDw+3dsXdwcHEmlREU1IWEWYhIhZhJqKIMHc3q61FBBMrM4sQ4GZuBiDnvFwuVVOEp5wPD5Yvv/LKH7z1N37vtde+8cpvX3vuOVEl4gBuvnMfT917/0YAjohwIIiIQQB5hJsTQCxCBKCVtt1MT06f3L334OHx8eNHp5eXq1KnadpN2+3UJg+r1qy1ZhYeER4RADwQ4eHhbubuZlMp4UEAmJjYzHbTbrPZrNfrWhuDNKe+71PSWdKUc/bwaTdtN9v1du1mLNLlfrEYcsrE5B61VUSAqLW23WwjfBiGcVyMi3Hoe1FVESJmkZyTiACw1na7yc2EtUu56/txHJeLg4OD5WK5HMZFlztJSVhYREVYVES6LndDPy6Gg+XYj33XZ1UBIQLuAYCBm+88wN6991/EzAOgo9vH2Lv33g04zMOrld20W68vLy4uzk7Wj8/WF+eby8fT5sKnLcOZIwmScsqJVVklmANwwEABAig8rDW35s3CA8KiKeecUmZVYgkAzMLCOktMqGYA5ZxUhIUIcDPzFm4AhEVUskrWWWKRpMrMAFqz3VSm3Xa7K82MmFLK47hIXaeSAJRq5g5iAOHezMzDzGqx0uo0TbXUqVQ3DwSISVPqx/Hg2uH169eff/5v/SeCp+69dwO/yfErZdqtLs6/uvPLD3/6448//tlnn3/24P699Wq93W7dLcwCiAiPcHMgmrlbuAd+AzOTsDAzMYBAzMzdzZqZm4UHCwNETEJMTAQiYQIxk3t4uLUr7g4OJtakIpqSsogwCxGxCDMRRYS5u1ltLSKYWJlZhAA3czMAOeflcqmaIjzlfHiwfPmVV/7grb/xe6+99o1Xfvvac8+JKhEHZuEcAG7dPsZTd37wAhBExCCAPMLNCSAWIQLQSttupienT+7ee/Dw+Pjxo9PLy1Wp0zTtpu12apOHVWvWWjMLjwiPCAAeiPDwcDdzd7OplPAgAExMbGa7abfZbNbrda2NQZpT3/cp6Sxpyjl7+LSbtpvtert2Mxbpcr9YDDllYnKP2ioiQNRa2262ET4MwzguxsU49L2oqggRs0jOSUQAWGu73eRmwtql3PX9OI7LxcHBwXKxXA7josudpCQsLKIiLCoiXZe7oR8Xw8Fy7Me+67OqgBAB97j1zgPs/fClt46+/xX2KLwCBBBAAI7evo+9O3/xwjRNu/X68vz8/PTk4uzR+vxsffFk2lx42QpCBZ1yzpr7nLrMKQlLMAwUxAEGyGaltFpbrW4OZlbNXZdyl1JWFRCDBESiwpKYYQ5iqCozCyHMmlu4mRsDzJQ0dUlTSiyiqsJJVJjJPWop26lsNtvaaiBEtO8HzVklRaA2m3kAAXczn0Uzq81qsT/7Z38Te99753+LAJhJsvbDsDw4vHb99n/zdezde+8G/m2OX9nuthdnZ3e+/PmPf/zBxx9//OXPvzg+Pl6t19Nua808nIkAeFwBEO61eTjANMNTBMJTgbji3szcrLYKIKcsIsyMp4iZiTUpADdrtU1lam4MsDCLqAqLCDGrMFESIRZh9is2a9UCISLKwiIEuFm4R4QmXQyLlNQjVNPBwfIbL3/ju9/9g1dff+N3vvnN555/gbMKcQA337mPp+69fyMAR0Q4EETEIIA84GYECAsRAahT3ay2Z4/Ofnn3/sMHJ09OT1eX62plKrtpt53q5GHNms3cwyPCAUSEByI8PNzN3N2smUUEAh4OwGqbSpmm3Xa7bdVIOKl0XaeaiCCqOWcA026apjJNWzMTkZTzOI45ZyEGYGYOMNO0my4uL0uZkqbc5eVi2fXd0PcqSszEpCLMTCwR7mYEVhbVNA7DMI7L5cFyXAyLcegHSVlUVfY0qahKyn0eFsNyMSyWi37suz6pCggIhAcAYhzdfoC9e++/iFkEQEdvH2Pv3ns34LCIKD5N0269vjw/Pz89uTh7tD4/W188mTYXXraCUEGnnLPmPqcuc0rCEgwDBXGAAbJZKa3WVqubg5lVc9el3KWUVQXEIAGRqLAkZpiDGKrKzEIIs+YWbubGADMlTV3SlBKLqKpwEhVmco9aynYqm822thoIEe37QXNWSRGozWYeQMDdzGfRzGqzWqyWXSmt1OLuEQAzSdZ+GJYHh9euP/e1F//d/zThqXvv3cBvcvzKdre9ODu78+XPf/zjDz7++OMvf/7F8fHxar2edltr5uFMBMDjCoBwr83DAaYZniIQngrEFfdm5ma1VQA5ZRFhZjxFzEysSQG4WattKlNzY4CFWURVWESIWYWJkgixCLNfsVmrFggRURYWIcDNwj0iNOliWKSkHqGaDg6W33j5G9/97h+8+vobv/PNbz73/AucVYgDV5wDwK3bD/DUnR+8AAQRMQggD7gZAcJCRADqVDer7dmjs1/evf/wwcmT09PV5bpamcpu2m2nOnlYs2Yz9/CIcAAR4YEIDw93M3c3a2YRgYCHA7DaplKmabfdbls1Ek4qXdepJiKIas4ZwLSbpqlM09bMRCTlPI5jzlmIAZiZA8w07aaLy8tSpqQpd3m5WHZ9N/S9ihIzMakIMxNLhLsZgZVFNY3DMIzjcnmwHBfDYhz6QVIWVZU9TSqqknKfh8WwXAyL5aIf+65PqgICAuFx850H2PvhS28dff8r7FFEwywIIABHb9/H3i/+4mtlmjar9erx4yenJ+enJ6snZ+vVed1ceJuUIgmlJH2Xui7noUs5syrAIRzEIAbIzWuZaimttXAHkWjKXZe0SzmLCJicGER8RUAAETOLCDMJEBHNLMwsDOHCLCJZhEVYhEWSZlUhpgi4RWt1V0przdwBFhVhIRaAIsLM7Up4uHu4uzVrbrW2P/tnfwt7//hP3geImCFZuy6Ny8Nnr//pP/oG9u69eyNwhbDHgOOpWG/Wp8cnX/zV5x988JeffvLJnTtfnjx8tF6vdtNkpTpcRQDCHjGFI8IDxMRgxl64R4R7BGIGj0C4u5mbtVoLgXOXkyZmBuDuAIiJRVJKRAiL0mqZJjNjIRIWvkLELCx8RUWYhJkCcLPamrUGIKckIkwCwM3Czc1FuesHFTG3JGlxsPz6rVtvvPnm62+8+a3Xv/3C0Uuas6oAOLp9H0/de/9GAB4zA4KImQggD0Q4ACEmEICyK5dPVicPz+7du3tyfPL40dlqva6tTGWqZVdasTB3b2bhFoEIjwgPRHhEuJm5uxkAFnGPWos1a63W2sys1NKqAdAkqpo0EZOZAWBiAO5Wq5VSIoxFcs6Lcez6PqmyCCJApCyllouLy81m26wx09APs3Ex5pyJGECEC7OoqoioCjEczNJ13TCMy8ViGIa+71PuJCXVrCwpa0o5payS8pAX4zAux+VyHMY+dUlVQLgS2Iuj2w+wd/cHLwAgIgBHf3yMvXvv3QiHeUS1Mk2b1Xr1+PGT05Pz05PVk7P16rxuLrxNSpGEUpK+S12X89ClnFkV4BAOYhAD5Oa1TLWU1lq4g0g05a5L2qWcRQRMTgwiviIggIiZRYSZBIiIZhZmFoZwYRaRLMIiLMIiSbOqEFME3KK1uiultWbuAIuKsBALQBFh5nYlPNw93N2aNbdaW7lSZ2YOEDFDsnZdGpeHz15//sUX/85/1uOpu+/eIOwx4Hgq1pv16fHJF3/1+Qcf/OWnn3xy586XJw8frder3TRZqQ5XEYCwR0zhiPAAMTGYsRfuEeEegZjBIxDubuZmrdZC4NzlpImZAbg7AGJikZQSEcKitFqmycxYiISFrxAxCwtfUREmYaYA3Ky2Zq0ByCmJCJMAcLNwc3NR7vpBRcwtSVocLL9+69Ybb775+htvfuv1b79w9JLmrCoAAnBcuXX7Pp66897XgCBiJgLIAxEOQIgJBKDsyuWT1cnDs3v37p4cnzx+dLZar2srU5lq2ZVWLMzdm1m4RSDCI8IDER4RbmbubgaARdyj1mLNWqu1NjMrtbRqADSJqiZNxGRmAJgYgLvVaqWUCGORnPNiHLu+T6osgggQKUup5eLicrPZNmvMNPTDbFyMOWciBhDhwiyqKiKqQgwHs3RdNwzjcrEYhqHv+5Q7SUk1K0vKmlJOKaukPOTFOIzLcbkch7FPXVIVEK4Ejm7fx94PX3rr6PtfRQQAimiYBQEE4Ojt+9j74i+eL9tpu1qtnjx+fHpycXa6enK6XZ3bbu11EoQKuiy5S12f+67TLrMkCIMlSEmECebeSrXW3A0eYGbRlLOoqiQiAQOEICECkYCJCcSscoUJCG8ecItwRBCBiZgZBBCpSEo9iTABIAQ8otmvhSMA0IyJCGB3q83czHwW7m7NmrVa65/987+Nve/9yQ9ADBaWxN2QhvHw2Wt/+o9ext7dd29gj7DHgAOImUesLs7v3vnq008/++CDv/zi88/v3b97dnq63W6naVdKCXdmIaYZg4hpFpgx8YwABtw9wt3MAzGDh3sE3MzNWq2VgNz1qirMAMzNA0wkLCkrMYdHszbtSoSRMBEJMzERMTEJMQsrz4SIAbhZtdZqA5BzTixgXHEgzJqzcE6ZVQBkTYuD5ddv3vy9b7322utvvPrmt49u3uyGXpOCcPT2fTx17/0bAXjMDAARMRFADkQEBZiIQBGx20wXjy8ePnx075cPTk6OH58+Xq1WpZZaJ2ulWbMwczOPcItAhAOICHMPD3ebuTuIk4q5T7tpmna1ttpqzBzEUJGu65ImFnGz2mqtzcMRAaLwKLXAMdOs47jo+y7nnDQRk4qIJmt1s9lsN9vttAsPFemHYTGOXd8TE2YRxJKTppkmZrZqRJxz7rt+GIau77ucU86SskpOWXNKOXcpZZWU+zyM3bAYl8vFOPTdkDUJCFcCe3F0+wH27r73NQDEDODoj4+xd/e9G+4I89asbKftarV68vjx6cnF2enqyel2dW67tddJECrosuQudX3uu067zJIgDJYgJREmmHsr1VpzN3iAmUVTzqKqkogEDBCChAhEAiYmELPKFSYgvHnALcIRQQQmYmYQQKQiKfUkwgSAEPCIZr8WjgBAMyYigN2tNnMz81m4uzVr1mqtU2m1lNqqm4MYLCyJuyEN4+Gz155/4aW/85/3eOruuzcIeww4gJh5xOri/O6drz799LMPPvjLLz7//N79u2enp9vtdpp2pZRwZxZimjGImGaBGRPPCGDA3SPczTwQM3i4R8DN3KzVWgnIXa+qwgzA3DzARMKSshJzeDRr065EGAkTkTATExETkxCzsPJMiBiAm1VrrTYAOefEAsYVB8KsOQvnlFkFQNa0OFh+/ebN3/vWa6+9/sarb3776ObNbug1KQgBeGB26/Z9PHXnva8BICImAsiBiKAAExEoInab6eLxxcOHj+798sHJyfHj08er1arUUutkrTRrFmZu5hFuEYhwABFh7uHhbjN3B3FSMfdpN03TrtZWW42Zgxgq0nVd0sQiblZbrbV5OCJAFB6lFjhmmnUcF33f5ZyTJmJSEdFkrW42m+1mu5124aEi/TAsxrHre2LCLIJYctI008TMVo2Ic8591w/D0PV9l3PKWVJWySlrTinnLqWsknKfh7EbFuNyuRiHvhuyJgHhSuDo9n3s/fClt47+6Z2IAEARDbMggEA4+uP72Pvsf75et7vtarU6Pz9/fHJxdra6eFzWl1Y2aIXDlEOEuywp59zlGacEYYgQJagwEwLwgDkAYiYWEhEmIgYIhACcmImIBSIAA87EkkRFhEGEWQQQjggQEI4ID4QbSFLOwgImAhMRgAiYh7uZeXgErhALsYR7sytuZh5uVpu1Wmqtf/bP/zb2vvfOD5gYrKxJ+yENy4Nnr/3Jf3sLe1/9qxvYYwIxrnjM3NytnZ4++uzTzz756KOf/PSnv/jy52dnjy4uLna77W5XprILMyJmJvdgJhEhZhALM5OQEEBAhLmHu0cgAEQEPNy9mbu1ahXgnBKLCBEACw8PgERIVIkYQIRbMw9nYmIiJiImphmDmIlZhJiFAJhZM2+1AVBRFQZABCZGeASIOSdNKTNx1/eHh4c3bt745jd/95u/9+o3X339xtGNcVykLoNxdPs+nrr7g5dA5IgZEAARwEQAOYIDBIoIs9iud+dnjx89fHT3/oNHxydnp2er1aqWqbbi3szNwszNAxGOmGEW4RFh/v9yBW+xtl7Xfdj//zHmnN+3Lnufw/vZ54gUZRGkKNKSIqtBUSNAXwIUDUiFNlwHDdCXvvmhKQr0rX1pC/QCBGjQl9Rq3MB5CVpUgkigTwnQlwKRTDlBSPEmy6Z4dC7iPvu29rp935xjjK616GMZ/f3cm1mEmQmhqma2Xm92xjqGOyA5a8ml9N10Oss5kWK1rrebcTsM4whAVQHUVs3MLUTZd33Xd13Xd6WkpCmXnJSkmY3juF6vazMhUi6TviulaEoqB6o555SSiiBQx0YwpdSVkku3V0ouJZc+pdKVkkvJpeRckmjuUu7KdNofHc2ms+lk2qeUKACBwB5x+80HOLj3w2cAUATA7Td+hYN7PzwxD29ex1Y3281yuby6uro4XZyfLxcX4+raxjXaKGFJQlW6ormU0pUdyRkqUCUzkooQAXjAHABFKEpVFZICEEQAThGSolAFBHChaNakqgISOxFAOCJAIBwRHgg3UHMpKgohISQBRMA83M3MwyOwR1GKhnuzPTczDzerzVoda63jTt1zD6FAkqSc+kmezI9uPvHUM8/+rf+iw2N3v38iBAV7Hjtu7tbOzh797JOfffzBB++9//4vPv2L8/NHi8Viu91st+MwbsOMFBG6hwhVlSKgqIhQqQQIRJh7uHsEAkBEwMPdm7lbq1YBKTmLqpIALDw8AKpSUyIFQIRbMw8XCoUUkkLhjoAiFFGliBKAmTXzVhuApCmpACAhFIRHgCIlp5yLULq+Pz4+Prlz8vLLr7z8tVdffvX1k9sn0+ksdwWCABwB4PnvPsRjn739HBAACQgJ0BESIBgRZrFZba/OLx59/ujeg4ePfnV6fna+XC7rONQ2ujdzszBz80CEI3awE+ERYe7ezCLMTAhVNbP1erMz1jHcAclZSy6l76bTWc6JFKt1vd2M22EYRwCqCqC2amZuIcq+67u+67q+KyUlTbnkpCTNbBzH9XpdmwmRcpn0XSlFU1I5UM05p5RUBIE6NoIppa6UXLq9UnIpufQpla6UXEouJeeSRHOXclem0/7oaDadTSfTPqVEAQgEbn/3AQ7evfWd23/4WUQAYERDACBAELffeICDj//ZzbrZrFer9eJqcXF+fXW5XlzU9TKGjdso0QhPRC6aS84l59JpThRFTkxZNVOVBAMKCAmKUEEAhMAdgEAAoYiSAlEAEQChSUWZREh8geEgBDvhjnBzNweTZoiCoKiCBBxwjwh3D3PskBAqhREwD7e9Ztaa1TrWnXH8/X/yt3Dwj3/nbVBFk+ai/bSfHc9uPPnGf/scDu5+/yQCJISgYCc83Dxaq7U+fHj/vX/73k9/+v4HH3xw/5f3lqvFdrMZh3EYh81247XiwD0oFN0TEYoKRVVwYOaBwAHBQOy5NzM3czcAIkoKhQDCAwjsUVUIUkgwEIEIBwQiwh0RACQFlAOlALDwPTN4iCrBQBBMKgTcXFQ0adJcSur76c2bN569des3fuOrL738yitff/3kzp350VHfd1Te/u4DPHbv7VsgAXgE/gohIA4YMAsz3yzXF+eXjz4/fXj/4enp2cWjs+vVchy2Y6sIs7CAm5sHIhwHpEQ4Ity9mYVHM4twRIy1blbrYRzHOgJIKfVdP5lOZpPpbD5LOQNota7Xm/VmU8cRQMqJlNZqra2Oo4fnlEspXVe60uWSUy4lJ1UVETPbbLfWmgeE0JRKzimlstN1JWdNCYCZubk3F4qqJs255K6UXEoupSuTlEvXl5xKzkVEVZlyziVNpv3R0Xw+n01mk9wVJSCEgwIQt998gIN7bz8LgCQCt9/8FQ7u/fDELFqzOtS62axXq/XianFxfn11uV5c1PUyho3bKNEIT0QumkvOJefSaU4URU5MWTVTlQQDCggJilBBAITAHYBAAKGIkgJRABEAoUlFmURIfIHhIAQ74Y5wczcHk2aIgqCoggQccI8Idw9z7JAQKoURMA+3vWbWmtU61p1xr9Y61ooIUEWT5qL9tJ8dz248+eTTT/32P0h47O73T4SgYCc83Dxaq7U+fHj/vX/73k9/+v4HH3xw/5f3lqvFdrMZh3EYh81247XiwD0oFN0TEYoKRVVwYOaBwAHBQOy5NzM3czcAIkoKhQDCAwjsUVUIUkgwEIEIBwQiwh0RACQFlAOlALDwPTN4iCrBQBBMKgTcXFQ0adJcSur76c2bN569des3fuOrL738yitff/3kzp350VHfd1QGYB4AXnjrIR67+/Yt/BVCQBwwYBZmvlmuL84vH31++vD+w9PTs4tHZ9er5Thsx1YRZmEBNzcPRDgOSIlwRLh7MwuPZhbhiBhr3azWwziOdQSQUuq7fjKdzCbT2XyWcgbQal2vN+vNpo4jgJQTKa3VWlsdRw/PKZdSuq50pcslp1xKTqoqIma22W6tNQ8IoSmVnFNKZafrSs6aEgAzc3NvLhRVTZpzyV0puZRcSlcmKZeuLzmVnIuIqjLlnEuaTPujo/l8PpvMJrkrSkAIx523HuDg3Vvfuf29uxEBgO4VACEgAdx+4wEOPvrjG3Wz2axW6+vl9eX5+vpqs7yq66Vt1m4DrAlMBFkll5RLzqVozsyZqWgpklJSFRHFngDuOGAIQCEJEVWlKERBxZ5HBACKkhDsuCMEEIBCVSEpgEeEuwU8AiBFVQSge4SbBzywI6SIgIq/xt3NrNZW99o4jsM4/v7/9ts4+Me/+45QmXb6PJ1Oj584uvnk3/6vnsDBvR+ceACEABTshLs1a7WOw3D3s8/e/fGfvP/+e5988vGvPv/VOGytNXMfx2GzXtdxDN+JZgYgqYqQqhRVyg4OAgGAIFVUBIB7uJm5mTkQ+DXiLwWAiADAHZGkShF4jN4QQYqKiApAAARVKBSqAIgIeHg4AKUCiHAAQgHCzYGgSFJNOc0ms6Mbx8899+zzL7z48itfe+2b33r+hS8f37jRTSaqvPPWQzx29+0TAUD8FUfgQEAADDTzNth6ubm8uHj0+aOHDx+enj66OLtYXl8P46a1GuEOD7iHRwQOuCcAItw8wm3Pvda23W6G7Xa92bTW3COpdF0/nUym8/lsNp3PZrkUeNRWV+vNMGzDg8JSCimtjpvtdr1a13EEoDl1XV9KyTmVvFNS0h0A7m5m7uFu5q4ipZR+Mjk+Pp50PVVqrevVqtaWJakmEUma8hdSzn3XdZNSSk4l5UQqRQCkpLkr09nk+Gh+dDSbzud9X0SFQgACUHj7uw9wcP+d5wBEBIA7b36Og3s/vGWGOrY61rrZbFar9fXy+vJ8fX21WV7V9dI2a7cB1gQmgqySS8ol51I0Z+bMVLQUSSmpiohiTwB3HDAEoJCEiKpSFKKgYs8jAgBFSQh23BECCEChqpAUwCPC3QIeAZCiKgLQPcLNAx7YEVJEQMVf4+5mVmure20cx2Ecx2Fba22teYRQmXb6PJ1Oj584uvnkjSef/Jt/EHjs7g9OBKBgJ9ytWat1HIa7n3327o//5P333/vkk49/9fmvxmFrrZn7OA6b9bqOY/hONDMASVWEVKWoUnZwEAgABKmiIgDcw83MzcyBwK8RfykARAQA7ogkVYrAY/SGCFJURFQAAiCoQqFQBUBEwMPDASgVQIQDEAoQbg4ERZJqymk2mR3dOH7uuWeff+HFl1/52mvf/NbzL3z5+MaNbjJRJQTmAeCFt36Fx+69c4IDR+BAQAAMNPM22Hq5uby4ePT5o4cPH56ePro4u1heXw/jprUa4Q4PuIdHBA64JwAi3DzCbc+91rbdbobtdr3ZtNbcI6l0XT+dTKbz+Ww2nc9muRR41FZX680wbMODwlIKKa2Om+12vVrXcQSgOXVdX0rJOZW8U1LSHQDubmbu4W7mriKllH4yOT4+nnQ9VWqt69Wq1pYlqSYRSZryF1LOfdd1k1JKTiXlRCqCgz0qAAAgAElEQVRFAKSkuSvT2eT4aH50NJvO531fRIVCAM+/9RAH7976zu3v3cUB3UeApAAEcPuNBzj42T+7OWyHzWq1vl4sr85Xi8vtYjGsr+t62eqW3hQugqJa+pK7nEuRlKmJJWnuJZecUxYBoIEvBA5IiHBHlZpVlaI48IC5AVBVEgg3N7eGcFKSMKesWZUEYB7mXpsBIikpJQAzq7W6mQdURFJKqoRCJNwisGNubtaatWZ1p7U6jr/7v/67OPjD3/u/SRFJkkueTKdHTxzdvPm3/+uncPDZ928RFGKH2ItwMx+2m8XV1c9//vM/+dGPPvjpB5/d/cXl+fnYRm/N3Mdx3K7XYx3drJm5GQBRJUWEhIgqhUISBEAhQVFNKgADYeZu5u4AAmFmQADEHoFw84ADEBFSc1bVFMBYa4SrqoiQgr1AYIekiggFQCB2CJCCiPAAQCE8ItzcBRTVnFPfT46O50899fTt23e++tLL3/j2t7/y1ZeefvqZ2dFck9x56yEeu/v2iRD/P44AICAABlrzYajrxeri7OL09Oz09PTs0aOri6vr6+vtZl3bCDgYDg9EBL5AYicCEW4e4bbnPg7D4no5bDe1mocBSClNun4ym853ZrPpdJpzBtDMhmGw1txDVLpSQLZa1+v1YrHYbgcAqtp1JeciqjmnrutySiCxE4EDj0AEAFGd9P386Kjv+6SpWVuv11abUoWyQ0pKKe+knEvX9ZPSlZyKJiUVQISLSO7KdDaZzadHR/Ojo+N+2peSRAWAABDe+e4DHNx/5zkAEQ7wzpuf4+DeD0/Mo1WzoQ7bYbNara8Xy6vz1eJyu1gM6+u6Xra6pTeFi6Colr7kLudSJGVqYkmae8kl55RFAGjgC4EDEiLcUaVmVaUoDjxgbgBUlQTCzc2tIZyUJMwpa1YlAZiHuddmgEhKSgnAzGqtbuYBFZGUkiqhEAm3COyYm5u1Zq1Z3WmtjuMwjq2OtTZzJ0UkSS55Mp0ePXF08+aNp575m3/geOzeD04AEHsRbubDdrO4uvr5z3/+Jz/60Qc//eCzu7+4PD8f2+itmfs4jtv1eqyjmzUzNwMgqqSIkBBRpVBIggAoJCiqSQVgIMzczdwdQCDMDAiA2CMQbh5wACJCas6qmgIYa41wVRURUrAXCOyQVBGhAAjEDgFSEBEeACiER4Sbu4CimnPq+8nR8fypp56+ffvOV196+Rvf/vZXvvrS008/MzuaaxIIzD0CX/6dz/HYvXdO8JgjAAgIgIHWfBjqerG6OLs4PT07PT09e/To6uLq+vp6u1nXNgIOhsMDEYEvkNiJQISbR7jtuY/DsLheDttNreZhAFJKk66fzKbzndlsOp3mnAE0s2EYrDX3EJWuFJCt1vV6vVgsttsBgKp2Xcm5iGrOqeu6nBJI7ETgwCMQAUBUJ30/Pzrq+z5patbW67XVplSh7JCSUso7KefSdf2kdCWnoklJBRDhIpK7Mp1NZvPp0dH86Oi4n/alJFEB8PxbD3Hw7q3v3P7eXRwwYgQIEGAE77z5AAef/vNnhmHYLlfLxeXi/Gxxcb6+vtosLjfLhW03QM2IkrX0ZTLpy6RLKSMlgBCVUnLpu64kVZgREKGIQJSiQkAICkgRhQhECYUg3GtrAHIpKkREa+N22Jo1CeSkpetKyZoUgDU3i2YGMOVCkXCvrW23W2vNHSJacklJRRWAu5uH257vhbm7RYQ3a2/+L9/Gwf/+H/8LOEIASVImZTKfP/HEm//dbRx8+n88I0mVRAQCOw4IY7FY3Lt79+OPPvzJT/70z3/+88uLs+vrxXaz3W43ZjYO4zhux51a3Sw8KBRVpTgoAhEhhSJKUoRCoSYVqpCMHQsPdw8g3GysNcJJoTA8dlprAahQvyCacnLAqgVCVEVFQAsPdzeHB4UUEYqqEAzEjpuHB4VCCvbcHACFO6JacpnPZsc3bzz11DMvfvWr3/gb33n5la+98OUXnnjyJpM+/9ZDPHbvnRP8NY4AICAeY6BVW6+H66vr80fnZ6fnFxfnlxdXi6vF8vp6uVq2OoAORjBAeAR24gtoZuG25+7NArHZbq8X18MwUqCiJDSlUsp0MpnNZtPptOv7nBJIHLh7uBPMXSHpZqvV6uLicrvdAEi5TCcTUY3wknM/meSUamtuexTpcsldKTkDaGYAkmoupe/7nDOAMK9DdXPskCKqol0pqXSl63LZU80qSiICJFLW0pfJdDI/mt+4cWM2n00mJWnCASVuf/chDu6/8xz2HODtNz7Hwb23T9wQ7l59GIbtcrVcXC7OzxYX5+vrq83icrNc2HYD1IwoWUtfJpO+TLqUMlICCFEpJZe+60pShRkBEYoIRCkqBISggBRRiECUUAjCvbYGIJeiQkS0Nm6HrVmTQE5auq6UrEkBWHOzaGYAUy4UCffa2na7tdbcIaIll5RUVAG4u3m47flemLtbRHiz1upOG8exmcERAkiSMimT+fyJJ55+9rl/7z9TPHbvByeIQGDHAWEsFot7d+9+/NGHP/nJn/75z39+eXF2fb3Ybrbb7cbMxmEcx+24U6ubhQeFoqoUB0UgIqRQREmKUCjUpEIVkrFj4eHuAYSbjbVGOCkUhsdOay0AFeoXRFNODli1QIiqqAho4eHu5vCgkCJCURWCgdhx8/CgUEjBnpsDoHBHVEsu89ns+OaNp5565sWvfvUbf+M7L7/ytRe+/MITT95kUgjcDMCXf+cUj9175wSAIwAIiMcYaNXW6+H66vr80fnZ6fnFxfnlxdXiarG8vl6ulq0OoIMRDBAegZ34AppZuO25e7NAbLbb68X1MIwUqCgJTamUMp1MZrPZdDrt+j6nBBIH7h7uBHNXSLrZarW6uLjcbjcAUi7TyURUI7zk3E8mOaXamtseRbpccldKzgCaGYCkmkvp+z7nDCDM61DdHDukiKpoV0oqXem6XPZUs4qSiACJlLX0ZTKdzI/mN27cmM1nk0lJmgDceesBDt699Z3b37uLA0aM2CPACN558yEOPvu/btXtuFmtVldXl2eni4vz1dX5enG5WV7buJFomcglTbrcTftcimqCSAMhqrmknErXZ1WEC6EUUYpkKCkiVBIQJRUCUEFCBO7NTSilFArDrdZxs1m3OsI9pdz3XVdySgqyNWstajNQUykqBNBa2w5jrRUeoHaliGpSjXDzcNtrZgiYO4AIhIe7/Z1/9E0c/NO//y/dIgAX1dTl6ezo5pN/9394Hgd//s+fSUUSJSI8gECER/ijR6c/++TjD376wfvvvX/vl5+tVsvNerVebYZx2+pYa6vjWGsd62DNwoNCUkQJiAgJiCpFlLJDlaRKOSA9/hI8AFRr4zi6magCCA+31twikHSPIlmTqAIwMwhJERUAEeFmvhcCqChVVAQHzcxrCw9VhZAeOKBQQFEBWEqeTCfz2fHNJ554/sWvfONbv/Xqa6+99NJLTz39lHb6/Fu/wmP33zkJ/JojAAgIwAOIgKPWtl5vF5eLs9Pzy7Pzi8ur66vFcrHcWa2uh3EADPRgOAI7EQBiD+5m7m5WW3Mzcx/HcbNaNzNVJUChqOac+34ym066rss5i6qQqppyBtBaA5BzFhFEbNab84uL7XZDMJc8mUwo4uYp6Xw+15SstVZbtSYifd9P+j6XAqCOo5mBzDnPZ7OudJpTeNTN2MwQgT2Kak5JNaeu5FxyLimpipIKgIRmLSX1k34+nx/fvHF0NJ/O+pxTOHYocfu7D3Fw/53nsOcAbr9xioN7b58g4I7wqNtxs1qtrq4uz04XF+erq/P14nKzvLZxI9EykUuadLmb9rkU1QSRBkJUc0k5la7PqggXQimiFMlQUkSoJCBKKgSggoQI3JubUEopFIZbreNms251hHtKue+7ruSUFGRr1lrUZqCmUlQIoLW2HcZaKzxA7UoR1aQa4ebhttfMEDB3ABEID3drzYZh3GlW3SIAF9XU5ens6OaTz9w6+e3/POGx+z84iQgPIBDhEf7o0enPPvn4g59+8P5779/75Wer1XKzXq1Xm2HctjrW2uo41lrHOliz8KCQFFECIkICokoRpexQJalSDkiPvwQPANXaOI5uJqoAwsOtNbcIJN2jSNYkqgDMDEJSRAVARLiZ74UAKkoVFcFBM/PawkNVIaQHDigUUFQAlpIn08l8dnzziSeef/Er3/jWb7362msvvfTSU08/pZ0C9HAAX/6dz/HYvXdOADgCgIAAPIAIOGpt6/V2cbk4Oz2/PDu/uLy6vlosF8ud1ep6GAfAQA+GI7ATASD24G7m7ma1NTcz93EcN6t1M1NVAhSKas657yez6aTrupyzqAqpqilnAK01ADlnEUHEZr05v7jYbjcEc8mTyYQibp6SzudzTclaa7VVayLS9/2k73MpAOo4mhnInPN8NutKpzmFR92MzQwR2KOo5pRUc+pKziXnkpKqKKkASGjWUlI/6efz+fHNG0dH8+mszzmF485bD3Dw7q3v3P7eXRwwYsQeAQZ4542HOLj7/RMb63a5Xl0vrs5Pr8/Prq8uhuVi2KyijiquRJdEk6asogpKiAQIVWrWpClpTjmrqmCPFApUKRRRkUxVEUIEO6QDQhGSKiklobRWh3HYbjZjHcMsKftScs6aFGR41Ga1GsBciqYsZCBas2YGB8ikShEAEXAzdzPfCQQi3EMiPDya2xv/6Js4+KO/9y+amUdAkpSunx0fP/nk3/3vn8fBL/7P5yRRhXBEhHm0Woft5sG9+++9/95HH3/4Z5988vmDh+vtZtist9tNraOZNTOvrdY6jsNYW4SHhygBEVUREhBViihFkgpFVUSVpJAAPMLdAZB0s2E7NDMKwwMIM2tm8BBVFYomVVFRANUtgCRK4U5E+F4IQFBEIMRBuDczq2M4VJRCAbgDUiiqAlKYU55M+9lsfnx884UXv/Kb3/qtV19//eWXX3762WdSl174nV/hsfvvnAR+zREABPRA7FiERx3qcr1eXF5fPro4P7tYLC4Xi+X6erWzXi+HcQCahzVvHh4RHlAhKSIEiQgzq7WNday1ugd2wiPC3MODwlJK1/WTvtOUcKAiKaV+MgFQhzEQpeuSKshhGBZXi2HYkpKSllIoYq3lnGfzeckFQITX1gCUXFJOOSWPqLUCSCl1pZvOpl0pFEEgmruHmwcCAZCESFLVlFJOSVWTqJIipIikkkrJ/aTMZvPjmzeOjubTWZ9zwmO3v/sAB/fffhY7DAC33zjFwb23TwhEwA021u1yvbpeXJ2fXp+fXV9dDMvFsFlFHVVciS6JJk1ZRRWUEAkQqtSsSVPSnHJWVcEeKRSoUiiiIpmqIoQIdkgHhCIkVVJKQmmtDuOw3WzGOoZZUval5Jw1KcjwqM1qNYC5FE1ZyEC0Zs0MDpBJlSIAIuBm7ma+EwhEuIdEeHg0N6t1GNs4jLWOzcwjIElK18+Oj5988plbJ7/9DxIeu//DEzgiwjxarcN28+De/ffef++jjz/8s08++fzBw/V2M2zW2+2m1tHMmpnXVmsdx2GsLcLDQ5SAiKoICYgqRZQiSYWiKqJKUkgAHuHuAEi62bAdmhmF4QGEmTUzeIiqCkWTqqgogOoWQBKlcCcifC8EICgiEOIg3JuZ1TEcKkqhANwBKRRVASnMKU+m/Ww2Pz6++cKLX/nNb/3Wq6+//vLLLz/97DOpS0qxCBAvvPUQj9175wSAIwAI6IHYsQiPOtTler24vL58dHF+drFYXC4Wy/X1ame9Xg7jADQPa948PCI8oEJSRAgSEWZWaxvrWGt1D+yER4S5hweFpZSu6yd9pynhQEVSSv1kAqAOYyBK1yVVkMMwLK4Ww7AlJSUtpVDEWss5z+bzkguACK+tASi5pJxySh5RawWQUupKN51Nu1IogkA0dw83DwQCIAmRpKoppZySqiZRJUVIEUkllZL7SZnN5sc3bxwdzaezPucE4PZ3H+Dg3Vvfuf29u4gAwIgRewQY4J03HuLg3g9OWm3jZrteXF+eP1penC+vzofVsm3XtJY0RJgEIhDAASdACRGIUhVMVCTVPqmIRhj2CBGK7qWiSUWVogAC8IAKU86iqqKAt9ZqHbfbTR3HCCOZU85Jd0giojUbmzmYc8kpS1KS4YFABHZIAogIczczBEB4ABEIRMADHu5mf+d//gYO/snf+5fVmnsEhLnk6ez4iad+/x/+Bg7+/J8/k4omEkAgrPmw2V5fLz79xaf/5l//6ccfffjpX3x6dnY2bLfjdjvWwc0C7h5u3qzWcax7LcIBkKCqihKgiKgqRZImVU1JZY8kDiICB81sHEY3c/dAmLlbM4+AE6JCUaWIUgJo1gIQEZAEA4EI7DgoVAoOLDzc3VqtBkBElBSAoqqyp6oiJJOmru8ms+nR0fHzL7z4+je+/fXXX3/la1979rnnuml54Xc/x2P33zkJ/JojAAjoEW7emnvzYTuu1+vF1fXF6fnl5dXO8no5rDab9Xq9WQ91cK/mtbk1t3ADQNGkKiqqCsDMWq3jONaxAihdIaW2aq3VZgBKSrnkUoqIupsHhEg5T/uJqAzjKGTXdTlnUa21rlarOo4gVURUEVFbU9Wjo6O+71NKALwZhDkloQDw8DpWCLtcukk/nU67UgASoiAAj/C9QGCPAFVUhbojqgJCJCUtJeUu9303m82Ob944OppPZ30uCYE94vabD3Bw/+1nscMAcPuNUxzce+eEAQTC0WobN9v14vry/NHy4nx5dT6slm27prWkIcIkEIEADjgBSohAlKpgoiKp9klFNMKwR4hQdC8VTSqqFAUQgAdUmHIWVRUFvLVW67jdbuo4RhjJnHJOukMSEa3Z2MzBnEtOWZKSDA8EIrBDEkBEmLuZIQDCA4hAIAIe8HA3q7UNw3hQqzX3CAhzydPZ8RNPPXfr1r//X07x2P0fnAAIhDUfNtvr68Wnv/j03/zrP/34ow8//YtPz87Ohu123G7HOrhZwN3DzZvVOo51r0U4ABJUVVECFBFVpUjSpKopqeyRxEFE4KCZjcPoZu4eCDN3a+YRcEJUKKoUUUoAzVoAIgKSYCAQgR0HhUrBgYWHu1ur1QCIiJICUFRV9lRVhGTS1PXdZDY9Ojp+/oUXX//Gt7/++uuvfO1rzz73XDctKmrYe+GtB3js3jsnABwBQECPcPPW3JsP23G9Xi+uri9Ozy8vr3aW18thtdms1+vNeqiDezWvza25hRsAiiZVUVFVAGbWah3HsY4VQOkKKbVVa602A1BSyiWXUkTU3TwgRMp52k9EZRhHIbuuyzmLaq11tVrVcQSpIqKKiNqaqh4dHfV9n1IC4M0gzCkJBYCH17FC2OXSTfrpdNqVApAQBQF4hO8FAnsEqKIq1B1RFRAiKWkpKXe577vZbHZ888bR0Xw663NJCNz+7gMcvHvrO7e/dxcRAOgxYo8AAd554yEO7v/wpFWrm3G9vF6cXVxfna8uzrebZYxbRitKJRQ7Zt6au0c4CVVQJamTEUjCnFRBcwt3xw5Fd1LpiuaiKUNUSA9EBIU5Z1WlIAK208axjtaquwFIIqKqokIg4O61RRCqmlPSlFWVJAiEBBDh7mHuboFwAKIKijsOiIgWcLP/8B++ioM/+vv/T6utuVtICEPL/PjGf/qH38DBR3983PddzomARXiz9XJ1fn7+sz/75Cfv/vijDz/85d175+dndRxbHc0NCBUCINDMxp1hbLU2qxHYoSoJQlQomnJOqinllFU156RKISl4LMLdbBzGatZqczN3t/Bwjx13ipBCoVIsvNVqAAERwYG7AxAIAVEBEBHuZh5hHmYUIamqAohoUhXVnBNFSKpIymkymUznx7e/9Pxrr3/z66+9/uqrXz/50u3pbPLl3zvFY/ffOQn8mgd2hDD31qyOzca23Qzr9Xpxtbh4dLWzWFyvl8thvd1sdza1Ds1G81qtNrfwACCqSTUl3QHg7q21se40Ifp+IsKxtlbHHXMnJammpCQjwgM7OWnXdaoKQFQnk0lXiqbk5tth21rDjkcgWmvDMIrKfDabTKd936eUSIqIqgIws7ozjAC6rptMJ9PZvOuKigo1qxKMCPOdQMDNAjDskBARUlQoIHJOpSulK31fZrPZ8c0b86PZbDbJOYH4wu03H+Dg3g+fwY5g584bpzi4984JA3uBVq1uxvXyenF2cX11vro4326WMW4ZrSiVUOyYeWvuHuEkVEGVpE5GIAlzUgXNLdwdOxTdSaUrmoumDFEhPRARFOacVZWCCNhOG8c6WqvuBiCJiKqKCoGAu9cWQahqTklTVlWSIBASQIS7h7m7BcIBiCoo7jggIlrAzdo4DuM4DONYa6utuVtICEPL/PjGM7duvfnfPIvH7v3ghIBFeLP1cnV+fv6zP/vkJ+/++KMPP/zl3Xvn52d1HFsdzQ0IFQIg0MzGnWFstTarEdihKglCVCiack6qKeWUVTXnpEohKXgswt1sHMZq1mpzM3e38HCPHXeKkEKhUiy81WoAARHBgbsDEAgBUQEQEe5mHmEeZhQhqaoCiGhSFdWcE0VIqkjKaTKZTOfHt7/0/Guvf/Prr73+6qtfP/nS7elskkpyx87zbz3AY/feOQHggR0hzL01q2OzsW03w3q9XlwtLh5d7SwW1+vlclhvN9udTa1Ds9G8VqvNLTwAiGpSTUl3ALh7a22sO02Ivp+IcKyt1XHH3ElJqikpyYjwwE5O2nWdqgIQ1clk0pWiKbn5dti21rDjEYjW2jCMojKfzSbTad/3KSWSIqKqAMys7gwjgK7rJtPJdDbvuqKiQs2qBCPCfCcQcLMADDskRIQUFQqInFPpSulK35fZbHZ888b8aDabTXJOIG6/+QAH7976zu0//CwiANBjxB4BOvj8Gw9xcO+HJ9asbutmuVpeXCwvzxeX5+Nq6XWrYVklCQCDW2u1ullEEBClJEnJyYggoSDDvZmFuQcAqkjKXelzKZqLJCUUQAAUppRFsRdwN3PzZh7mbggHIYBQ8FgEAFJEdC/pHkUDcIe5uVsYPBDhFMm5CNXxBSLgEc3sP/ifXsbBP/1P/l9rtVpYoAVaYDKd/8Ef/zs4eP+PZtPpJJcsBCLMfHl9ffr55x9/8tGPf/SvPvzgw7uf3b08v2g2uhngJDWlRKEyzMbaxp061jq6BeAQJUGICkVTzinnnHLOKeWcRTWpck8oDI8Ib83MWq112A7NLBB77uFh4eFBoZAELbyZuTsAEXF3AAHAAwcEAQQiwsMjzAlQhKSqCiCiOaekKZWcVCNChKJaSj+dzm6d3Hnltde/9trrv/n6b37phS8dHc9e/I/O8Nj9d04Cv+aBHSGs+VBrHcZhM2422/V6s7xaXJxfLa6ul9eL1Wo9roe9cVvr0Gyo1jzMwzywo0IRUVWKYCfC3N28mamwdJ2IuHurdRjGsY5uRlBzTqokdiJAQh/LOfeTSSkl5wyg1RbhANy91joMw3a7JTmdzSY7fZ9LySnnkncA1FqHYdhuNs0spTSdTObzo34yKaVkVaESBBABxA5sJ9w9IuAOEZAKUiiatO9L6Uvflel8fnzjeHY0n88mpSQQX7j95gMc3P3hM/CQRAB33jjFwd23bwmIwI41q9u6Wa6WFxfLy/PF5fm4WnrdalhWSQLA4NZarW4WEQREKUlScjIiSCjIcG9mYe4BgCqSclf6XIrmIkkJBRAAhSllUewF3M3cvJmHuRvCQQggFDwWAYAUEd1LukfRANxhbu4WBg9EOEVyLkJ1fIEIeEQzs1qHsY3DuB1Ha7VaWKAFWmAynT9zcuv3/sfn8djd758IgQgzX15fn37++ceffPTjH/2rDz/48O5ndy/PL5qNbgY4SU0pUagMs7G2caeOtY5uAThESRCiQtH/jyy4C9n1vPLD/l9rXeu67+fjffbe2vraW5EsbUljS5Y9jiQPPcpBG1JokcQYStzA5CAJpc1AodBCoaQHTeegtFNIS6CxwwSGNjANxViCQuhRToLtUTyutfXlTOyR5L3fLe/9fj1f98d1rbV6v49mTybN75dUk6omVU1JVVkkidAlJqbwiPBazayWUoZ+qGaBuOQeHhYeHsTERASy8Grm7gCY2d0BBAAPHBAIQCAiPDzCnABiJiIRYYBZVFOSlLImkYhgJhbJuZ3PF0/eeOrLX33lK1995WuvfO0vPPMXjlYLbTIcYDz11jEeuvPODQAemDDBqg+llGEcurHr+v2+216sz04v1heb7Wa92+3H/XBp7EsZqg3Fqod5mAcmwsTMIkLMmESYu5tXM2HKTcPM7l5LGYZxLKObEUhUkwgRJhEggjykqu1slnNWVQC11AgH4O6llGEY+r4novliMZu0reasSTXrBEApZRiGvuuqWUppPpstl0ftbJZzVhEmIRCACCAmsEm4e0TAHcwgEhAxsSRp25zb3DZ5vlyurqwWR8vlYpZzAuHmm8c4ePfJ129851OPgAdVHzAhAiiCnnnzcxx89r0b4V6GMmx3m7PTzfnp9vxs2G3q0IlXYQiB4R4eXmu4BYJAIiyJREECBjzgpdYaVquZmxvATJxUc6O51ZxTSpKUWXBAIiAAjgAQgEcE3IHwSxYeCMOBkHASkCAAApGIkIgws4Pcw8zcPAACA0gpSc7CCgJAIEbAAu72l3/nWRz847/1h7WWYlHMi8do3s4Wv/3738TB7d9bzOczbZQBAgKx2Ww/v/f5xx998IMf/OD927f/5Oc/Pzs7s1IBJ4CFNWtOiYXD3cxKKeOkFCvV4QADYCYCWJJqUtWkmnNWSSySkrCIEBNjUs3DbVJK7fu+loqHPLyahTsOwiMQFoEJESLCIxBmFo4Ig2MSCAARjomDmUHEzELEzEkmSVVzVmYJBAACqWozmz/25I0Xf+2ll7/6ytd//RtfevaZ1bXV898+xUN3374RhH9brdb149D1/Z+FQXcAACAASURBVL7v9v1+320u1hdnF+v1ZrPZ9vuu9OMwDmUchjrUMphXcIDwrxHhISYCQEQAeCKXkkg1GyZ93w9jhGfVfMDM1S65OQDVlHNu2jZrzlmJGQfMbGZD33cHAJq2bZpGkzZtM5/PF/NFO5sRoZay77r1ej30vXvkJq+OjhbL5dFyqZph+AKRMOBAhLtFMXP3iHAHMxExiFLS3Oa2ybnNi8XsypUry9XRfDFrGiUAhMnNN49x8Nn3H4MHBJOn33yAg0+//wRAcBAo3MtQhu1uc3a6OT/dnp8Nu00dOvEqDCEw3MPDaw23QBBIhCWRKEjAgAe81FrDajVzcwOYiZNqbjS3mnNKSZIyCw5IBATAEQAC8IiAOxB+ycIDYTgQEk4CEgRAIBIREhFmdpB7mJmbB0BgACklyVlYQQAIxAhYwN3KpVrGcRjGWkuxKObFYzRvZ4tHH3/sr/5Pz+Khz753gwECArHZbD+/9/nHH33wgx/84P3bt//k5z8/OzuzUgEngIU1a06JhcPdzEop46QUK9XhAANgJgJYkmpS1aSac1ZJLJKSsIgQE2NSzcNtUkrt+76Wioc8vJqFOw7CIxAWgQkRIsIjEGYWjgiDYxIIABGOiYOZQcTMQsTMSSZJVXNWZgkEAAKpajObP/bkjRd/7aWXv/rK13/9G1969pnVtdWsaYIxeeqtYzx0550b+HNqta4fh67v93237/f7bnOxvji7WK83m82233elH4dxKOMw1KGWwbyCA4R/jQgPMREAIgLAE7mURKrZMOn7fhgjPKvmA2audsnNAaimnHPTtllzzkrMOGBmMxv6vjsA0LRt0zSatGmb+Xy+mC/a2YwItZR9163X66Hv3SM3eXV0tFguj5ZL1QzDF4iEAQci3C2KmbtHhDuYiYhBlJLmNrdNzm1eLGZXrlxZro7mi1nTKAE33zrGwbtPvn7jO596BDyoeA/QJIIQeOatX+Hg0+/dgHsdyrDbbc7O1uen27OTfrutQ4c6MFwoCAEKIAIIwInAwimR5KQqInArY2+1mFU3q+YeAQJLSilLbjRfSpqZhZMwEQgeEwAOBAFMQMDD3LxaDSvhEeEEsIhqFuIaHgECE1NKykwOhlup5gEE6JLoJDciGgQQgdgDEXCzf/fvPo2DP/jP/t9SxlKtmBeLsVpuZ7/9+9/Ewe3fW8zmM9VEBCAm64v18Z07H374wQ9/+IP3b9/+5Bd/cnF+Xs0IIQTRlCcpJREA5lZrHYexWi2lelQA4fgCiySRpKpZs2pKiUWSXCKWJAygmocbgKGM/a4rVolImFkkzEupNQwegQiLwAHTBEBEuLu5h1kt5mHhEeHh+AIxCCASAmQCSEqas6rmnJnJPXCQJOWmffTxJ2698OWvvPzKN/7iq8/deu7ao1df/GtneOju2zeC8G8bxrLf9fvdvtvt9/uu2/e7zX59frHZbCf9vq9DKWUsZSh1HMtgUVkAAhHhICI8AgfCByJCzMIAmFlVPWI46Lo+3JpJeymJlFrLOA7D4BFNzppz0zSqyswiklhYRLO62b67NPQ9gKZpRFIgmpyXR0fL5dFyudQkpVq3352dnW9322EYmHk5X6yurK5cvdrmxmowQBNmIgEQ4W5R7JI7AiAQMxNTSqJZc5vbJs+XyytXVkero8Vy3qgSA4TJzTePcXDn+4+5R0gA9MybD3Dw6fcfhxNAAMG9DmXY7TZnZ+vz0+3ZSb/d1qFDHRguFIQABRABBOBEYOGUSHJSFRG4lbG3Wsyqm1VzjwCBJaWUJTeaLyXNzMJJmAgEjwkAB4IAJiDgYW5erYaV8IhwAlhENQtxDY8AgYkpJWUmB8OtVPMAAnRJdJIbEQ0CiEDsgQi4WalWyziUUsZayliqFfNiMVbL7ez6Y499+3efxUOf/l9PEgGIyfpifXznzocffvDDH/7g/du3P/nFn1ycn1czQghBNOVJSkkEgLnVWsdhrFZLqR4VQDi+wCJJJKlq1qyaUmKRJJeIJQkDqObhBmAoY7/rilUiEmYWCfNSag2DRyDCInDANAEQEe5u7mFWi3lYeER4OL5ADAKIhACZAJKS5qyqOWdmcg8cJEm5aR99/IlbL3z5Ky+/8o2/+Opzt5679ujV+azFhPHUW8d46M47N/DnDGPZ7/r9bt/t9vt91+373Wa/Pr/YbLaTft/XoZQyljKUOo5lsKgsAIGIcBARHoED4QMRIWZhAMysqh4xHHRdH27NpL2UREqtZRyHYfCIJmfNuWkaVWVmEUksLKJZ3WzfXRr6HkDTNCIpEE3Oy6Oj5fJouVxqklKt2+/Ozs63u+0wDMy8nC9WV1ZXrl5tc2M1GKAJM5EAiHC3KHbJHQEQiJmJKSXRrLnNbZPny+WVK6uj1dFiOW9UiXHzrWMcvPvk6ze+8yki3INqDLjECPKIL731Kxx8+r0nYVFLHba7zdnp+uxkfXbabS6s39vYwyvBBeBEKTGxgDmIAEZKKTeaW82JEGPfeRnd3MLM3NwDATAkJVFts2rW3GrOqomFPYBw80CYBxgOAhAwN7NSx2o1zBAgQFiaNoOoWoQbQMScNDEzQO6oxQIRQUScUpKkWTNJAjEIAYqAeVSzv/x3n8bBH/z2T0upZWJWLQZDM2v+9j96HQfv/cN5O5/llIjCzUspZ+enn33y2QcfvPfDH/3wow8/PL57b7Newx0IEZIkeSJJNRHDPNys1Gq1llLdzeBhHuEBEEAiWTWlJCmpCMslZk5yCSAPQ4CYx1J2u62VyinlJKoZwFjGWi0Q4eHm7mBhYiYiEGESUc281lKqlVqshEeEh4MYlxwEEDMRCbOI5InmplEiCQTCI0AsucmPPPrkc8+/+JWXXnn1tdduvXDr+qPXv/xb53jo7ts3gvD/44G+H7frzXay2Xf7ru+Hbtdt19vtZrvb7vp+qEOxMhYrtY6lDh4GAQhMBMAjEJcAEF9SSZwkpQTA3YkoqwIYxrGM4zAMANrZbNa2s9mMmMs49sPQdR0imqbRnFWVmeHBwppUc27ahom6ri/jOJSRiXLTRMQwDMy8Orq0Wq1yzgC6rjs9O7u4uFiv12bWNs3y6OjatWuzdgYHg5iZSJgJgHuYu9WoYQiAQCTCMmGRlCQ3ktvmaLlYXb26OjqaL+dto2AiwuTmm8c4uPP9xwA4B4Cn33iAg0++9zguEYFgUUsdtrvN2en67GR9dtptLqzf29jDK8EF4EQpMbGAOYgARkopN5pbzYkQY995Gd3cwszc3AMBMCQlUW2zatbcas6qiYU9gHDzQJgHGA4CEDA3s1LHajXMECBAWJo2g6hahBtAxJw0MTNA7qjFAhFBRJxSkqRZM0kCMQgBioB5VLNqVoqVMpZLtUzMqsVgaGbN1Uce/Wu/+ywe+uT/fIIo3LyUcnZ++tknn33wwXs//NEPP/rww+O79zbrNdyBECFJkieSVBMxzMPNSq1WaynV3Qwe5hEeAAEkklVTSpKSirBcYuYklwDyMASIeSxlt9taqZxSTqKaAYxlrNUCER5u7g4WJmYiAhEmEdXMay2lWqnFSnhEeDiIcclBADETkTCLSJ5obholkkAgPALEkpv8yKNPPvf8i1956ZVXX3vt1gu3rj96fb6YgTB5+jeP8dCdd27gIQ/0/bhdb7aTzb7bd30/dLtuu95uN9vddtf3Qx2KlbFYqXUsdfAwCEBgIgAegbgEgPiSSuIkKSUA7k5EWRXAMI5lHIdhANDOZrO2nc1mxFzGsR+GrusQ0TSN5qyqzAwPFtakmnPTNkzUdX0Zx6GMTJSbJiKGYWDm1dGl1WqVcwbQdd3p2dnFxcV6vTaztmmWR0fXrl2btTM4GMTMRMJMANzD3K1GDUMABCIRlgmLpCS5kdw2R8vF6urV1dHRfDlvGwXTU28d4+DdJ1+/+Z1PIwIA1RgAYpCDIvDMm5/j4NPvPQkPL9Zvd+uzs/Xp/fXp6f7irPS7OvZRRsASQ5PknFkTmAMcIEo5NblpZ03bMKEMndeKcI8ws+pWHeGYUEpJVXNum1lucmqysHh4eLibu0U4IogwCbdqVmsJr+6OCAIxc9YMwKo5HGBhSqokwiAPmJk7AiASlommlCECIoAjYO5mUcz+/d95Ggf/+G//dBzHUmupVquXCG1m//nvfxMHf/QPctu2KSUgrNah7+/fv/+LX/z8/fff+8Mf/eiP//hfXZye7rsO7uAQIkmiKammJCIsAMzNzYpZuBerZrVadbNwB8AiSZJoUkmcJIkwUVJlEU2JCNUMgIqOpWy2G6s1a27apm0bAg9jX4oFItyreThABCICmJmIIsLcrdQ6lrFcMqsRHg5iXDJMiEAiDKhIbpqcc9NkIglEuEcEi6g21x59/Eu3Xnj5pVdeff2151944fpj17/y1y/w0N23bwThzzMPN9/vurOLi816u9/u+m4Yx7Hfd7ttt9/u9rv90A21VKul1mJeio0eBg4g8G/iAxFhuZREAJRaAWhKHlFrtVrNTERms1nTNJozgDKOwzgOw0BEs9lMVROLI0opABrNuW0W8wUzDePoZtVMmHPTWKnr7QYRi8lyeXR0NGtbFhnHcX2xvrg4Pz097fqeiObz+bWrV+fzReKUWAgMJiYG4GYWqGYemDCTiCYRFmGZkGZtmnx0tLxy7erqymqxnDWqxATC5Oabxzi48/bjAJwcoKffuI+DT77/BBwTAsHDi/Xb3frsbH16f316ur84K/2ujn2UEbDE0CQ5Z9YE5gAHiFJOTW7aWdM2TChD57Ui3CPMrLpVRzgmlFJS1ZzbZpabnJosLB4eHu7mbhGOCCJMwq2a1VrCq7sjgkDMnDUDsGoOB1iYkiqJMMgDZuaOAIiEZaIpZYiACOAImLtZFDOrVqqNdSyljONYai3VavUSoc3s6tUrf/1/+TU89C//j0eAsFqHvr9///4vfvHz999/7w9/9KM//uN/dXF6uu86uINDiCSJpqSakoiwADA3Nytm4V6smtVq1c3CHQCLJEmiSSVxkiTCREmVRTQlIlQzACo6lrLZbqzWrLlpm7ZtCDyMfSkWiHCv5uEAEYgIYGYiighzt1LrWMZyyaxGeDiIcckwIQKJMKAiuWlyzk2TiSQQ4R4RLKLaXHv08S/deuHll1559fXXnn/hheuPXZ8v5iCA8MxvHuOhO+/cwIF5uPl+151dXGzW2/1213fDOI79vtttu/12t9/th26opVottRbzUmz0MHAAgX8TH4gIy6UkAqDUCkBT8ohaq9VqZiIym82aptGcAZRxHMZxGAYims1mqppYHFFKAdBozm2zmC+YaRhHN6tmwpybxkpdbzeIWEyWy6Ojo1nbssg4juuL9cXF+enpadf3RDSfz69dvTqfLxKnxEJgMDExADezQDXzwISZRDSJsAjLhDRr0+Sjo+WVa1dXV1aL5axRJaabbx3j4N0nX7/53c8iAgB5jLhEDgrQM2/cw8Fn378Bjxis2+/Xpyeb0wfnJ/d3F+el29Wh83EAjBma0qxtJSuzOJM7kWpqmraZzReLJLAyhhngjjD36lHN3NwswMRJVVPTzJq2zTmTSJhZuJuZW7h5BOML7h7uhjBMAkyYMBEifAIwsYgkzSkJEUWgWni4BwBmFmIhFrAAEgFzN4vRzKr9h//Dszj4/f/kj0oZh1JLsWJWzDTn//IP/hIO/vnf89lspknCvYzjfr///PN7P/vZz27f/um777772SefjP1Qao1wohAQMyVNkkSZSYRwKSLc3dzN6jiOpQzFzN0AMIuKJFWRpJqIWVOSJJpyVgVQrABQzVbr+cWFV2vapm1ny+WCmIdhsFo93D3crJq7hbkzM02AACKi1lrGUksZx6EW87BwEOOSgwBipgmgIqq5aZucM7MEAuERIJbc5EeuP/70cy+8/NJXX/vmb9x64flHH7v+5d86x0N3374RhD+vVuu6YbPdnp+db9ab/a4b+7FWG4Zx2Pfdrtt33dAPPlarpXo1q47qYRbVwycAmIiZhSVpYrnERCAiUCDcHREel2qtAEQkq7azmaoCCPOhjG5m7qq6XCxUlYnHWvb7vZmpapOb5XIpScwMARA0pZzzWOr5+VkppW2adjZbzOdt2zZNExG7fbdeX5w8eLDebEopKaWjo6PFfLGcLVQzEQOIAMLdw9w9wgPMMkkpadIkiRKDoDnN2na1Wl65dm11ZbVcznJWfIFw881jHNx95wkAAQfw1Bv3cfDp208iAAcxwSMG6/b79enJ5vTB+cn93cV56XZ16HwcAGOGpjRrW8nKLM7kTqSamqZtZvPFIgmsjGEGuCPMvXpUMzc3CzBxUtXUNLOmbXPOJBJmFu5m5hZuHsH4gruHuyEMkwATJkyECJ8ATCwiSXNKQkQRqBYe7gGAmYVYiAUsgETA3M1iNLNqtdpYSrFay2QcSi3Filkx05yXR1f+03/463jo9u/Nw72M436///zzez/72c9u3/7pu++++9knn4z9UGqNcKIQEDMlTZJEmUmEcCki3N3czeo4jqUMxczdADCLiiRVkaSaiFlTkiSaclYFUKwAUM1W6/nFhVdr2qZtZ8vlgpiHYbBaPdw93Kyau4W5MzNNgAAiotZaxlJLGcehFvOwcBDjkoMAYqYJoCKquWmbnDOzBALhESCW3ORHrj/+9HMvvPzSV1/75m/ceuH5Rx+7PlvMEADhmW8d46E779zAQa3WdcNmuz0/O9+sN/tdN/ZjrTYM47Dvu12377qhH3ysVkv1alYd1cMsqodPADARMwtL0sRyiYlARKBAuDsiPC7VWgGISFZtZzNVBRDmQxndzNxVdblYqCoTj7Xs93szU9UmN8vlUpKYGQIgaEo557HU8/OzUkrbNO1stpjP27ZtmiYidvtuvb44efBgvdmUUlJKR0dHi/liOVuoZiIGEAGEu4e5e4QHmGWSUtKkSRIlBkFzmrXtarW8cu3a6spquZzlrABuvnWMg3effP3mdz/DAXmMuEQOCtAzb9zDwZ23b8DCa/S73fr05OLB/bOT+7vz82G/sX7vtcAtCVRT07ZJlUSCOECUNDVN287axUxF4BVhRIiAh5vHaObFqnsESFg0N5fanDOJ1FrCw93cLcI9AgATiHDgQADgAAMIuJuHIwIgYeaUVHNKAmKA3MIAn4ABCmKAQAxIdTeLYlZrqcXe+t0XcfCP/uaPSxmHsYxWS7VixpL+m+/9ezj4Z/9jP5vPNSWEj8Ow2+2Oj+99/NEH791+78c//hd37tyJsbo7EIQgBgEsE54kYQKIGUC4Ayhmw9iPQz+WGuHhQUxJRFSzKouklDSlpCkfMLhaIXBu8jiM5+fn1WrTtov5/Gi1YuJh7K2Yw8PC3Gq1sVi4BxAR7h5ARHi1WqvVWsZS3cIcE8aEHMQMgIkIYOasmnNumkwkgUB4BJhFm/aRRx9/9tbzL738tVdf+40XXrx1/fq1F3/rHA/dfftGEP6MB8Zx3G525xfr87OzzXq733V1LOFRqw3dOHT9vuuGfvCxWK3Vq8MI4bBqxcIQgQmRiKSUNCUWEWYcuEcgCBQIdzczd2fmrDk3uW1bJjazatVqBSAimvNsNlNVZhmGfrvelFpEpGna5XIpSSJAhCSSUlLVUsr52fkwDqqaVNtm0s5mM2Lyapvt9uTkZL2+2O/3HjFr2/l8sVqumtwSYRKBiU8iwgEWYWJR1aRJkyRKDILmNJ/PjlarR65dXV1ZzRetasKfiptv3cPB3XeeABBwAE+9cR8Hn759AwAFmAALr9HvduvTk4sH989O7u/Oz4f9xvq91wK3JFBNTdsmVRIJ4gBR0tQ0bTtrFzMVgVeEESECHm4eo5kXq+4RIGHR3Fxqc84kUmsJD3dztwj3CABMIMKBAwGAAwwg4G4ejgiAhJlTUs0pCYgBcgsDfAIGKIgBAjEg1d0silmtpRYbq1m1YrUWK2UcxjJaLdWKGUuaLxb/xf/+7+ChP/oHGeHjMOx2u+Pjex9/9MF7t9/78Y//xZ07d2Ks7g4EIYhBAMuEJ0mYAGIGEO4Aitkw9uPQj6VGeHgQUxIR1azKIiklTSlpygcMrlYInJs8DuP5+Xm12rTtYj4/Wq2YeBh7K+bwsDC3Wm0sFu4BRIS7BxARXq3WarWWsVS3MMeEMSEHMQNgIgKYOavmnJsmE0kgEB4BZtGmfeTRx5+99fxLL3/t1dd+44UXb12/fq1ZzBAA4ZlvHeOhO+/cAOCBcRy3m935xfr87Gyz3u53XR1LeNRqQzcOXb/vuqEffCxWa/XqMEI4rFqxMERgQiQiKSVNiUWEGQfuEQgCBcLdzczdmTlrzk1u25aJzaxatVoBiIjmPJvNVJVZhqHfrjelFhFpmna5XEqSCBAhiaSUVLWUcn52PoyDqibVtpm0s9mMmLzaZrs9OTlZry/2+71HzNp2Pl+slqsmt0SYRGDik4hwgEWYWFQ1adIkiRKDoDnN57Oj1eqRa1dXV1bzRauaANx86xgH7z75+s3vfoYD8hhxiRwUoGfeuIeDO2/fIENY9Lvd+enJ2YMHZ/fvbc/Phu3a+p6iAp6IVEVTYk0sAhawUMra5NQ2TdNqEoQJGROB2IEIVLNazapZAATRlLXNOacmA6jVJvhTBoCJiFkYzMQEIkwoAo5wCzMPB8DEzCSirCmJkhDAEaiBcLdABMLZgAAiyDxKtVJtLONY7D/6ey/h4Lt/491SapnUWqqVUkn477z9V3Dw//z366PVUZMzCGUc97v93eM7H3/04fu3b//kJz+5e+eX4zC6GSEAEAKMCREJiIWIWZhpAhBzsToM/TAOpRQ3cw8CsYiqqKpISiI6aTTn3OZWkrg7Czc5D+N4cv/BWGvbNovFcrU6Skm7bm/VQAj3Us2qVbNSzaqVesnNIsIPwsLNAg6AwMQ0AYgABgGBCCYWTaqaNRFJIBAOgERynl1/7PHnX/jyS6987dXXXn/u1q3rj155/j8+w0N3374RhC94IAJd11+cn5+eXZyenm7X674bbDQwuYUNNvT9fr8fuqGWYrWaV1CIAOTFzcMQAYAncsDCwjhw80AAUElg8ol5IIRZc9YDIipjcTMPZ5FmkrNIkiTMUsbx/OJ8HEcRaXKeLxYpqbsxiyZJKUnSUsb1el1KEWaWS03OTds2OTPL0Pdn52fnFxfbzaaUIinNmtnR8qjNDYgIBDAzJu4IEAkzyySlrElYhEWEoU1eLGar1dHVRx45Wh3N501SQQCI8HjqNz/Hwd13ngAQcABPvXEfB5++c4MCEwbIEBb9bnd+enL24MHZ/Xvb87Nhu7a+p6iAJyJV0ZRYE4uABSyUsjY5tU3TtJoEYULGRCB2IALVrFazahYAQTRlbXPOqckAarUJ/pQBYCJiFgYzMYEIE4qAI9zCzMMBMDEziShrSqIkBHAEaiDcLRCBcDYggAgyj1KtVBvLOBazatW8eNRaSqllUmupVkolYW3a//qf/CU89Ef/m4JQxnG/2989vvPxRx++f/v2T37yk7t3fjkOo5sRAgAhwJgQkYBYiJiFmSYAMRerw9AP41BKcTP3IBCLqIqqiqQkopNGc85tbiWJu7Nwk/Mwjif3H4y1tm2zWCxXq6OUtOv2Vg2EcC/VrFo1K9WsWqmX3Cwi/CAs3CzgAAhMTBOACGAQEIhgYtGkqlkTkQQC4QBIJOfZ9ccef/6FL7/0ytdefe31527duv7oldy2EZg8861jPHTnnRseiEDX9Rfn56dnF6enp9v1uu8GGw1MbmGDDX2/3++HbqilWK3mFRQiAHlx8zBEAOCJHLCwMA7cPBAAVBKYfGIeCGHWnPWAiMpY3MzDWaSZ5CySJAmzlHE8vzgfx1FEmpzni0VK6m7MoklSSpK0lHG9XpdShJnlUpNz07ZNzswy9P3Z+dn5xcV2symlSEqzZna0PGpzAyICAcyMiTsCRMLMMkkpaxIWYRFhaJMXi9lqdXT1kUeOVkfzeZNUELj51jEO3n3y9Zvf/QwHVGMAiEEOCtAzb9zDwZ23b5AhPPrd/vzkwdmDB6f3723PTvvNRR16gQuBGSpEzMJCSSCJk4oq51ZzlpySMMOEKAkTEZgjUN3NwqqZezCLSEpZc1JVB0opiGAmJiYGiISYmBIzCwkz4IgwtzCHGSJwIExETCycZMIsYAHgAXO4w8PNoxoC8IAZSi1jqcNYSq1/9X99BQff+Zvv1lJLtXKpDqV4xH/3f/8HOHjnv31wdXXUztrEUmvd73fHx/c++uiDD95//72f/vTunV92fWdjJQ5QIAIIeOCAhYWZiFlY+JK5lzKO4zCWcSyjWwBgpiQiqkmERVRS0zZZc9PmCUvKmjQ3wzD86lefD8M4a9v5Yn51dVWSdPuuWMHEUWpxx6SYDf3Q9/3QD6UUj4AHAcQEgIhFeELMRIQIOA4i3AmUkrBI1gTicI8IACwpN+1jT9x48csvffWVr7/62mvP3Xpude3o+W+f4qG7b98Iwhc8EIZ91z14cHJ6enJyerpZb8d+cAMzwVGLlX7Y7/ddN9gwllrdKzhUmZmcHAhMiJgIRARiJhy4R4R7BBOJCBPjzzCJSEpJRMLDzTwcQEo6n81EU7gTs6patYuL83EsLNzkPJvPNSX3YCZNykmEpZRxv9uVWokPQKKpbWdtzppzNdtu1hfr9fr8YhgHEDVJ23aeNQsxmJMwiIUZJCBiZmJhlkssxJSSSOKmaebL2epo9cij149Wy6bVlASB8HD407/5KxzcfecJAAYD6Ok37uPg03duIEAAA2QIj363Pz95cPbgwen9e9uz035zUYde4EJghgoRs7BQEkjipKLKudWcJackzDAhSsJEBOYIVHezsGrmHswiklLWnFTVgVIKIpiJiYkBIiEmpsTMQsIMOCLMLcxhhggcCBMREwsnmTALWAB4wBzu8HDzqIYAPGCGUstY6jCWUmsxdwsL+XXf3QAAIABJREFUL2a11FKtXKpDKR6hKf2dt/8KHvrDv0+Jpda63++Oj+999NEHH7z//ns//endO7/s+s7GShygQAQQ8MABCwszEbOw8CVzL2Ucx2Es41hGtwDATElEVJMIi6ikpm2y5qbNE5aUNWluhmH41a8+H4Zx1rbzxfzq6qok6fZdsYKJo9TijkkxG/qh7/uhH0opHgEPAogJABGL8ISYiQgRcBxEuBMoJWGRrAnE4R4RAFhSbtrHnrjx4pdf+uorX3/1tdeeu/Xc6tpR2zQWmDzzrWM8dOedGx4Iw77rHjw4OT09OTk93ay3Yz+4gZngqMVKP+z3+64bbBhLre4VHKrMTE4OBCZETAQiAjETDtwjwj2CiUSEifFnmEQkpSQi4eFmHg4gJZ3PZqIp3IlZVa3axcX5OBYWbnKezeeaknswkyblJMJSyrjf7UqtxAcg0dS2szZnzbmabTfri/V6fX4xjAOImqRtO8+ahRjMSRjEwgwSEDEzsTDLJRZiSkkkcdM08+VsdbR65NHrR6tl02pKgsDNt45x8O6Tr9/87mc4oOI9QEQMUICeeeMeDu68fQMGeOx3+/XJydmD+yf3P9+enuwuzmzss5AwhCAITJhIhJKmnDnnlBtJmRIJgwmJOYkQCQuB2ANm7m4e4SBmlpSSKicBYNUASBJNwiLCwgRhYRFmCCHCarVSSx1HRKiIsBAABkAgZiYwCwlYwIJABNzhbsW9erjDAmY+1jqOVsaxFPv2338FB9/9Wz82s1KsltKPYz8M1ex3/ukbOPgn/9Vn165cmc/nqinMu767d+/exx9/9MEHH3z4/u1f3vlsv9mWOoIABBAwD0R4AMHMxCzELJKSMFEAFlHrOPT9OA7VzN0JREKJhUWIOYkk1TY3TdvM2tlsMZu1bdM0+677/Pjzvu+10cVieeXKSph3+85qBRAetRoR5aZ1t+1mt93vttvtMIxmBkCYkkw0qyZNkpKmRECpFuaBgLmHw4OFiVlTAuDmFg4gScq5ffypmy9/9etf+/o3vvHqa1969ktHq8Wtb5/goTtv38CEMPGAG3bb/f0H9x88eHByerrbbPt+hENEELBiZRh3u93Q9cMwWinVKnNok5KKMLEwCxNzRCCimrl5hF+KwEMRISKaEotg4uEIAAwC4AgGiSZVnc1mAIZhYKLZbObm2+221spJmtzM57Mkyd1ArCmxMIBare86d08pERMCSURzbprctrNwdEO33W7XFxfD0EeAQEzMLMKSNGlSEWUVFhESZgGBSAgTgrCK5JyaebtYzFdXVtevX18eLbRJSRhARJjZM9+6j4O77zwBoEYF6Jk3H+Dg03duIDARAAZ47Hf79cnJ2YP7J/c/356e7C7ObOyzkDCEIAhMmEiEkqacOeeUG0mZEgmDCYk5iRAJC4HYA2bubh7hIGaWlJIqJwFg1QBIEk3CIsLCBGFhEWYIIcJqtVJLHUdEqIiwEAAGQCBmJjALCVjAgkAE3OFuxb16uMMCZj7WOo5WxrEUq241EGHVYGalWC2lH8d+GKoZgN/5p2/goX/+P1fVFOZd3927d+/jjz/64IMPPnz/9i/vfLbfbEsdQQACCJgHIjyAYGZiFmIWSUmYKACLqHUc+n4ch2rm7gQiocTCIsScRJJqm5umbWbtbLaYzdq2aZp9131+/Hnf99roYrG8cmUlzLt9Z7UCCI9ajYhy07rbdrPb7nfb7XYYRjMDIExJJppVkyZJSVMioFQL80DA3MPhwcLErCkBcHMLB5Ak5dw+/tTNl7/69a99/RvfePW1Lz37paPVommyBSbPfOsYD91554YH3LDb7u8/uP/gwYOT09PdZtv3IxwigoAVK8O42+2Grh+G0UqpVplDm5RUhImFWZiYIwIR1czNI/xSBB6KCBHRlFgEEw9HAGAQAEcwSDSp6mw2AzAMAxPNZjM33263tVZO0uRmPp8lSe4GYk2JhQHUan3XuXtKiZgQSCKac9Pktp2Foxu67Xa7vrgYhj4CBGJiZhGWpEmTiiirsIiQMAsIREKYEIRVJOfUzNvFYr66srp+/fryaKFNSsIAbr55jIN3n3z95nc/wwEV6/H/UQV3Mbae133Y/2utZz3vu/eemTMzhzyHcwhSlkhRH+CHZChIgBhBIaI3BUhBTmorNZLGTdA2KYpetChQFEUL5MJ1bLdB0gtbLtygSdGb1IIoFG2BwAiS2DAkypL4KVJGTJ5zyDlnPs/M7K/3eZ61Vvfe4jjO70dEYBBH0NOvHmLj3msHMITHMFtcnZ09Oj05OX5wcXo8f3TehkUiqIARgCPcARZhVe177XrNPSUJAhEnoSSiopxkhZkcHOHmEe4Ag0EiwkJCAALhASYSYUmiKWnSJMJMDAbc3dxabbWWBkCTJBFiwbUAiAjEzMIsANzh7tXMPcy9xQqaeWtRWq2l1Wbf+IfPY+Obf+t7brBmpZblUBbzea3tV37v57Hxf/xn7+/t729vbXVdR0Sl1OOjh++//9677779xhtv3L/34dX0qpQlYSUCjvCV8EAEiBhMiVUSiwgziDzCWi2l1DpUs/AAiBgMEJiYSDiL5pU+TyaTne2d0WTUaR6GcnxytFyWpDrqu8lkwsSz+azUCkeE1WpC0o/7cExns+l8NpvOhmEwMwDMlFVzyrnLOWvSnFQJqLW6Wbi5RTODO0DERMRAuDkQAEhS7ronDu58/osvvPDil7705S//zKd/5sbejWf+6hmu3fvWAQtAWLEWtfn0anZ0fHR8cvLo/NH0ajqUAoemDMBqHRbL+Wy+WCzqYllbbdaYoZ1qlqRJJYkKEwMwd2+tmZmvRXgEgMAaJRHNOYl4hJvVVt08AAJYWFiSrnWq5jEsF8S8s7PDoNlsWmtj4a7rtsYTVbVwACoCYg+vrdWhIJxZwEwI5qQqOXd91xNhWcuwWEynszoM5uHhbs7ELClpWpMsWTRlkSTMICDgTmAWoaQraTzut7a3dm7s7O3tjbfGSVmECYgId3/q60fY+Pg7twE0byB6+tUTbNx97SAAAgiAITyG2eLq7OzR6cnJ8YOL0+P5o/M2LBJBBYwAHOEOsAirat9r12vuKUkQiDgJJREV5SQrzOTgCDePcAcYDBIRFhICEAgPMJEISxJNSZMmEWZiMODu5tZqq7U0AJokiRALrgVARCBmFmYB4A53r2buYe4tVtDMW4vSai2tNqvNmpuHm4cbrFmpZTmUxXxeawPwK7/387j2L3912XUdEZVSj48evv/+e++++/Ybb7xx/96HV9OrUpaElQg4wlfCAxEgYjAlVkksIswg8ghrtZRS61DNwgMgYjBAYGIi4SyaV/o8mUx2tndGk1GneRjK8cnRclmS6qjvJpMJE8/ms1IrHBFWqwlJP+7DMZ3NpvPZbDobhsHMADBTVs0p5y7nrElzUiWg1upm4eYWzQzuABETEQPh5kAAIEm56544uPP5L77wwotf+tKXv/wzn/6ZG3s3+r5zAIGnfv4Q1z76zoG1qM2nV7Oj46Pjk5NH54+mV9OhFDg0ZQBW67BYzmfzxWJRF8vaarPGDO1UsyRNKklUmBiAuXtrzcx8LcIjAATWKIlozknEI9ysturmARDAwsKSdK1TNY9huSDmnZ0dBs1m01obC3ddtzWeqKqFA1AREHt4ba0OBeHMAmZCMCdVybnru54Iy1qGxWI6ndVhMA8Pd3MmZklJ05pkyaIpiyRhBgEBdwKzCCVdSeNxv7W9tXNjZ29vb7w1TsoiTMCdVw+x8foTX7nz2/ewQdUHgIgYwR741NcOsXH3tQNYuMUwX84vLy/Pzk4fPnh0+vDi9KQuZwwXGOAIQzMHIJy6bjQZ96Nx7nviVD0I0JxzSqoqKSVVZgFhxQMrATjhpyKcmCWliFgsFhGek3ZdnoxHqhkr4eGBcI8It9oMAZI1FhEWAjzcPLBCEBJmAuAWrVlpxdwBdsDdzcMBNx9qq8V+8R88j43f/I/+0B3WrNSyXCym01kt7df/1V/Fxj/6W2/v7e3d2Lkx2drKmj3i9PTkj3/y3ptvvfFH3//en3zwwXR6MQxLQgQc8ICH+RoCASZeE00sJAKE25o3czgiHNc8AmvCLCKaNWseT0a7u3uj8Vg1ebPpdFZbYZaVrGpuy8WylGpurVqzBkA1ARiGUmodhsFqbWZwkJBKUtXcZc2dZs0pAbBmzVoz82rmrZkDgUB4BMIjAAgRSUqqjz/+xHOf+8IXvvj8Cy+99JlnPnP74Pbn//oVrt393QMRECGAWm0xrxeXV0fHx6enZ48enc1ni1orA5oUQC11sVjMrq6Wi/mwHGop5o0ImpOqJk26IolFsOEIRLhHhLtZszVm7nJWVUkJoHArtQ6lWGtuwUIpJU1JWFgEgJvXWrquu3nzpopML6dDHQBo1+1s73RdxgYRRUQzs2a1FncHEQMgEpaUsyZRSQCqW62tDstWm4W7ubkBrFlFBETMIqqac587YfGIcLcIkZS7PqkI02g82tu7sXPjxs72dj/qWIiEmIiAQDz56gNsfPyd2wBaGEBPv3qMjQ++fUAAEdYs3GKYL+eXl5dnZ6cPHzw6fXhxelKXM4YLDHCEoZkDEE5dN5qM+9E49z1xqh4EaM45JVWVlJIqs4Cw4oGVAJzwUxFOzJJSRCwWiwjPSbsuT8Yj1YyV8PBAuEeEW22GAMkaiwgLAR5uHlghCAkzAXCL1qy0Yu4AO+Du5uGAmw+11WJDLbVWi/CAO6xZqWW5WEyns1qacPrVf/Hv49o//7uzydZW1uwRp6cnf/yT9958640/+v73/uSDD6bTi2FYEiLggAc8zNcQCDDxmmhiIREg3Na8mcMR4bjmEVgTZhHRrFnzeDLa3d0bjceqyZtNp7PaCrOsZFVzWy6WpVRza9WaNQCqCcAwlFLrMAxWazODg4RUkqrmLmvuNGtOCYA1a9aamVczb80cCATCIxAeAUCISFJSffzxJ5773Be+8MXnX3jppc8885nbB7cn21sMOPDU1w9x7aPXDmq1xbxeXF4dHR+fnp49enQ2ny1qrQxoUgC11MViMbu6Wi7mw3KopZg3ImhOqpo06YokFsGGIxDhHhHuZs3WmLnLWVUlJYDCrdQ6lGKtuQULpZQ0JWFhEQBuXmvpuu7mzZsqMr2cDnUAoF23s73TdRkbRBQRzcya1VrcHUQMgEhYUs6aRCUBqG61tjosW20W7ubmBrBmFREQMYuoas597oTFI8LdIkRS7vqkIkyj8Whv78bOjRs729v9qGMhEmKiJ189xMbrT3zlzm/fwwZVLwQCMYI88KmvHWLj7ncOooVZlMWwuLq6PDs/Pfr44uTo0dlxnU/hjcMIDliYY0VYu268NelHo9yNwFLNQKKaNWdNK6o5swgI/wbBsWbh7sFMmrWaTa+mrRTVNOr78dZWpwkR8JUAwIADbhYAWFhSSkoiBESYm3sEAGFiZgBhaLXW1swNgIc3jwiAxMNbs2Vpv/j3n8fGb/7yH7hFNaulLOaL6XRm1X79D34JG7/zy2/s7u7v3tjb3t7u+56A0/OzP37/vR+99YPvffe7H3zwr2ezi1oHIALuYeEe8DB3DwcYRCzMiUlEGIBZc3NsEBOuhXt4ACCmJEk0ZdV+NNrZ2ZmMR6o5wkupzZowEzFAzepysSil1tas1VqbuzNzuLcIb9asuXm4AyAmFkmSsqbcZUmqKQFwt2Zm1Zq12pqbR3h4WHh4ACCmJEIiSfSxm7effe5zX/zi8y+89NKnP/vsnYMnPv8fXuHa3d89IAIzzDEM9epqfnb+6Ojo5NH52cXFxWKxcHcm7jQBKLUsF8v59Goxn9eh1FbMDAhi0pRUNekaMwsxVpiw4e5m1lqz1kDUdZ2qMjEAN6utrrRmAFi4y1lSYmYAZubNrLW+6/b39yWlxXxhrYIo57y9tZW7DsREYMACtRZrZtZ8JQIAE4lISklEWAQRzcya1VqstWbmHoEQEc2ZhT3AxKyScz/qRizibgiYu4iOJiPVTIzJeLS3v7t748Z4Mu66DAYxmFawcueVQ2x8/J3bAFo4gZ569QgbH3z7gAAQiBAtzKIshsXV1eXZ+enRxxcnR4/Ojut8Cm8cRnDAwhwrwtp1461JPxrlbgSWagYS1aw5a1pRzZlFQPg3CI41C3cPZtKs1Wx6NW2lqKZR34+3tjpNiICvBAAGHHCzAMDCklJSEiEgwtzcIwAIEzMDCEOrtbZmbgA8vHlEACQe3potSxuG5VBbBHzFoprVUhbzxXQ6s2rC6Vf/1S/i2u/9D7Pt7e2+7wk4PT/74/ff+9FbP/jed7/7wQf/eja7qHUAIuAeFu4BD3P3cIBBxMKcmESEAZg1N8cGMeFauIcHAGJKkkRTVu1Ho52dncl4pJojvJTarAkzEQPUrC4Xi1Jqbc1arbW5OzOHe4vwZs2am4c7AGJikSQpa8pdlqSaEgB3a2ZWrVmrrbl5hIeHhYcHAGJKIiSSRB+7efvZ5z73xS8+/8JLL336s8/eOXhisrPFWHvy64e4dvdbB8NQr67mZ+ePjo5OHp2fXVxcLBYLd2fiThOAUstysZxPrxbzeR1KbcXMgCAmTUlVk64xsxBjhQkb7m5mrTVrDURd16kqEwNws9rqSmsGgIW7nCUlZgZgZt7MWuu7bn9/X1JazBfWKohyzttbW7nrQEwEBixQa7FmZs1XIgAwkYiklESERRDRzKxZrcVaa2buEQgR0ZxZ2ANMzCo596NuxCLuhoC5i+hoMlLNxJiMR3v7u7s3bown467LYBCDiZ589RAbrz/xlTu/fQ8b1KyAiMFOFIGnXz3Ext3vHMDRapTFsJheXZ6enR59/Oj06Or8dJhNEZXDhIIpCCAmFtGc83jc973kLohaDbB0OatmSUmSJhVmcWwQmAAiEDsB8PAgIk5SaruaXtZSBNzlPJqMVZXDEYEAEZgEgLk7GLKWNIsIARFuZhFBADFEhECIcLPWrLXazMzDwyIIxBFobkOxX/ifn8fGb/7y75t5KW0ow3KxHObL1vzXfv8/wMbv/I03dnf3dm/sb21vd7kDcHp68v5P3nvzzR+8/vp3P7z7wXI5q20APGAIDzeDh0UEwgEQgcDCJMxEQDiAAIKYABARQFiLNQ8WZhFNKUnq+jyeTMajcc5KxMNQgGAmEBORtTabz4dhWWtrpdZaqlm4u7kjVgCEBwBiEmIWTimJCIskEWICEI4Iq82srZibOWLNAxssnERSSpLSY48fPPfcF59//oWXfvZnn3n2M/uP3fzsLz3CtXvfOgAQAXdM58vzs/Pjk9OT4+Oz80fz2ayWgnAW6ZKC0WodhmExn5dhaLW4NTNrtiZMST+RREBrDMIKEzzMrbbmZh7BRMRrANw9fA1EIpJ1JasmAObeSjVr3kxERqORiEQEM2tSzdrlLCnxmmgSd9Raaqvu4W4RgQgQCbGoJhEQEKi1DOUTtTUAupa1y8wMJhCLiKbUdyNhAeCIAEnSfjTKOYnw1mS8t7+3s7M9now6VTBAIMJP3XnlEBsfvXYLgEeA6KlXj7Dx4WsHWCEQAEerURbDYnp1eXp2evTxo9Ojq/PTYTZFVA4TCqYggJhYRHPO43Hf95K7IGo1wNLlrJolJUmaVJjFsUFgAohA7ATAw4OIOEmp7Wp6WUsRcJfzaDJWVQ5HBAJEYBIA5u5gyFrSLCIERLiZRQQBxBARAiHCzVqz1mozMw8PiyAQR6C5DcWGUmqtzcNtxUtpQxmWi+UwX7bmidOv/MtfwLXf++9nW9vbXe4AnJ6evP+T99588wevv/7dD+9+sFzOahsADxjCw83gYRGBcABEILAwCTMREA4ggCAmAEQEENZizYOFWURTSpK6Po8nk/FonLMS8TAUIJgJxERkrc3m82FY1tpaqbWWahbubu6IFQDhAYCYhJiFU0oiwiJJhJgAhCPCajNrK+ZmjljzwAYLJ5GUkqT02OMHzz33xeeff+Gln/3ZZ579zP5jN8fjETHC8eTXD3Htg396MJ0vz8/Oj09OT46Pz84fzWezWgrCWaRLCkardRiGxXxehqHV4tbMrNmaMCX9RBIBrTEIK0zwMLfampt5BBMRrwFw9/A1EIlI1pWsmgCYeyvVrHkzERmNRiISEcysSTVrl7OkxGuiSdxRa6mtuoe7RQQiQCTEoppEQECg1jKUT9TWAOha1i4zM5hALCKaUt+NhAWAIwIkSfvRKOckwluT8d7+3s7O9ngy6lTBAIEId145xMbrT3zl4Jt3sUG1DsTExE4cgadfPcTG3e8cwOEthmVZXk0vz09PHx6enzy8PDtZzK7glcOUkYQ1MYuQcNKc+y7lLqmCpJoTieZeNaWknGQFLPhTDGYBA0QgIAIrIrWU6dV0KEsEUtK+7zQpA0JYYSIRATjCnRgsIklUmYUJvmLNzIFggJMQEUd4uJu1Zs02YgVEEoCFt9p+/jeex8Y3/+YftGpDKcuVxbCcD1791/7wl7Dx23/tR/t7+zdu3NjZ2VXN4XZ8cvz++z9+860ffv/7r9+//2GpC7MGmMMiLMIR7h4eCEeAEAAYoDWAQQQCBxEBxMJMhA2PNSJKzCyiqjnnrstd33ddL0y1tggXERADqKUuFrNhKLXVWlura6UUdwdAIGICiJiEmIWJWZiZiEQIIGZsuLu1FXMzCw8PILBBzEIsmlRUNT126/Yzz3zhhRdf+nN//i888+ynt3d3PvONM1y7962DCDQLa3E1nT58eLx2cnp5ebFcLs2aACyimgFvtZShlGGwUpq3MGvNzFqtFQhVFUlJk4gwEYiYiJgZBMAR4Wvma/hTER4BQERyzt2GiCCimZVSWqluFu4ARKTrur7vR6NREsEKkYhkVc0ZwDCU1ioizFcC7gBYWESYBQQ3q6UOZVgsl2U5lFaZuev7ruu1zykpCKAVTinl3CcRBjkA5pRS3/U5Z80ynoxv7u/t7GyPJp1qAuHPuvPKITY+eu0WADMnpqe+doyND187AIGw4fAWw7Isr6aX56enDw/PTx5enp0sZlfwymHKSMKamEVIOGnOfZdyl1RBUs2JRHOvmlJSTrICFvwpBrOAASIQEIEVkVrK9Go6lCUCKWnfd5qUASGsMJGIABzhTgwWkSSqzMIEX7Fm5kAwwEmIiCM83M1as2YbsQIiCcDCW22LYrVVN2tmrdpQynJlMSzng1dPIr/y+9/AtX/2313u7Oyq5nA7Pjl+//0fv/nWD7///dfv3/+w1IVZA8xhERbhCHcPD4QjQAgADNAawCACgYOIAGJhJsKGxxoRJWYWUdWcc9flru+7rhemWluEiwiIAdRSF4vZMJTaaq2t1bVSirsDIBAxAURMQszCxCzMTEQiBBAzNtzd2oq5mYWHBxDYIGYhFk0qqpoeu3X7mWe+8MKLL/25P/8Xnnn209u7O33XgbFy52uHuPaT//P21XT68OHx2snp5eXFcrk0awKwiGoGvNVShlKGwUpp3sKsNTNrtVYgVFUkJU0iwkQgYiJiZhAAR4Svma/hT0V4BAARyTl3GyKCiGZWSmmlulm4AxCRruv6vh+NRkkEK0QiklU1ZwDDUFqriDBfCbgDYGERYRYQ3KyWOpRhsVyW5VBaZeau77uu1z6npCCAVjillHOfRBjkAJhTSn3X55w1y3gyvrm/t7OzPZp0qgmEn7rzyiE2Xn/iK7d/60MmAkC1zEEkkkBsoKdfOcTGvdcOIhCGsizDbHZxfnZ6dHh69ODR8YP59BK1MFyVO5UuZ9VEIklTksw5SVIncgdY+65TzZxkhZhBFPgEMbMICxEzCNeotja9ulwuBw8XYs1ZNSViIhJmEVERJnEARM4skliUmQhhblbMvLobEEICAhM8EG6+FhZwtwgQSRBFRDP72t/7AjZ+5z/+XqllWJT5YraYLxezpVX/n77317Hxm994fe/mY7srO7uq2lp7ePTwvfd+/OZbP/zhD1//+KP7zVtEDbeAOSzCA4FYoXB4RDjCEEB4EBODiYkJxETELCzMhLUAIoKIeEUkiSRJSbXrc5d7Ea6twkFCBAbQWp3NZ6UUq9as1dZaqUMZzAxEBBLhNRFhphVmImJaw59h7m7WzNw83HGNmIg5iahqSlk17d+89enPPPvCi1/+iz/3c88++8xkZ/KpXzjBtXvfOnBDqT7UdvHo4uPDB8dHD0/PzubTWbWGcCEWERYO81KWtZZaqrUWbuHWmplbaxURKSURYWYQMRGImFk2eIUYn4jamrsjwiPcHQARaUpd3/ddPxr1LAnhrbXlMLRarbYyDPP5XET29/a2tre3JlvEtFwOcNcudzn3fU9EwzDUZhEeEW7mgQgnYmZiYgDNrAzL5TAs54uhDEOtzDwejUfjUdd3rEpEIAaQRDTllBLxioBJknaa17o02Zrs7e3t7GyNx11SAeHPuvPKITbufftxAGENRE9//Qwbd79zgMAKAREIQ1mWYTa7OD87PTo8PXrw6PjBfHqJWhiuyp1Kl7NqIpGkKUnmnCSpE7kDrH3XqWZOskLMIAp8gphZhIWIGYRrVFubXl0ul4OHC7HmrJoSMREJs4ioCJM4ACJnFkksykyEMDcrZl7dDQghAYEJHgg3XwsLuFsEiCSIIqKZDcWqmZvVZqWWYVHmi9livlzMlladRX79D38J1/6///bR7s6uqrbWHh49fO+9H7/51g9/+MPXP/7ofvMWUcMtYA6L8EAgVigcHhGOMAQQHsTEYGJiAjERMQsLM2EtgIggIl4RSSJJUlLt+tzlXoRrq3CQEIEBtFZn81kpxao1a7W1VupQBjMDEYFEeE1EmGmFmYiY1vBnmLubNTM3D3dcIyZiTiKqmlJWTfs3b336M8++8OKX/+LP/dyzzz4z2ZmoKhgrd752iGtv/e83Lx5dfHz44Pjo4enZ2Xw6q9YQLsQiwsJhXsqy1lJLtdbSDhrpAAAgAElEQVTCLdxaM3NrrSIipSQizAwiJgIRM8sGrxDjE1Fbc3dEeIS7AyAiTanr+77rR6OeJSG8tbYchlar1VaGYT6fi8j+3t7W9vbWZIuYlssB7trlLue+74loGIbaLMIjws08EOFEzExMDKCZlWG5HIblfDGUYaiVmcej8Wg86vqOVYkIxACSiKacUiJeETBJ0k7zWpcmW5O9vb2dna3xuEsqIPzUnVcOsfH6E1+5/VsfYoMWi0th5qTMYqCnX3mAjXuvHSAQjjbUYbG4enR+evTw9OjB2dHH88uLaIPANUmX02jUqSZiJhYmoSTEEizmkJRG/ShpZhYSZhYQHAiACMLCSViEBAABYGYAtZbpbLZcDm5GQNKkSRMLMyWRlFRFhAVMDg4ALCtEhAgzq7VYq9aahzMxEZgICA/8VASaWwSIBKBANPdXf/Xz2PhHf/v1MrTFfDGfz6bT+eJqXpv9wx/8TWz8g7/yh489dnNv77EbOzdS0joMD48evPvu22+/88aPfvTDwwcfB6qHhZujAR7hoIgAgsIQCDOEhbmbOQFJhEBgEFMSYRFhZiIAAUQEETEzEaUkRJJEkmo/6pmothbmJIyNWupyuSi1hplHtGbN2rAc3Boxg0iYmZiYiUmIiWkFIPxbYqWZhXt4BAIbBCJmFk4iqpo1p5z3928+9dSnX3jhy3/pq1/97GefGU1Gn/qFY1y7962DWqMMbbEcTs/P79+/f3R0/OjR+XKxBMBMmoSZAbRah2FZy2C1uRsi3M1tzd2BABETgQgbTCuckuhK0pRVRJgYQLNmzdzMwlcQwcyS0qjvc+760UiTAGhmtdZWax3Kcj6/vLxkllu3b+3u7m5NJu6Yz6fNrFvJ/WjUgbjW0qpFmLmHh7thg1kAeLiZDT+1WA611FpZZDwedd2oG3WqChFsMLGknGSNWVhEUpKkOacu58nWZH9/d2dnazTqNCesEP7UnVcOsXHv248z0TAshPlTf/kCG3dfO8AGAQiEow11WCyuHp2fHj08PXpwdvTx/PIi2iBwTdLlNBp1qomYiYVJKAmxBIs5JKVRP0qamYWEmQUEBwIggrBwEhYhAUAAmBlArWU6my2Xg5sRkDRp0sTCTEkkJVURYQGTgwMAywoRIcLMai3WqrXm4UxMBCYCwgM/FYHmFgEiASgQzb2VqG7NrbZahraYL+bz2XQ6X1zNazNh+fvf/xu49v/8N+c3dm6kpHUYHh49ePfdt99+540f/eiHhw8+DlQPCzdHAzzCQREBBIUhEGYIC3M3cwKSCIHAIKYkwiLCzEQAAogIImJmIkpJiCSJJNV+1DNRbS3MSRgbtdTlclFqDTOPaM2atWE5uDViBpEwMzExE5MQE9MKQPi3xEozC/fwCAQ2CETMLJxEVDVrTjnv79986qlPv/DCl//SV7/62c8+M5qMVFNg7cmvH+LaD357+/T8/P79+0dHx48enS8XSwDMpEmYGUCrdRiWtQxWm7shwt3c1twdCBAxEYiwwbTCKYmuJE1ZRYSJATRr1szNLHwFEcwsKY36PueuH400CYBmVmtttdahLOfzy8tLZrl1+9bu7u7WZOKO+XzazLqV3I9GHYhrLa1ahJl7eLgbNpgFgIeb2fBTi+VQS62VRcbjUdeNulGnqhDBBhNLyknWmIVFJCVJmnPqcp5sTfb3d3d2tkajTnPCCmHlziuH2Hj9ia/c/q0PATARTS9PmZN2WVLy4KdefYCNe68dMICA1TYsyvTi4vTk4fnxw0fHD+ZXF14HdlOVPqe+z0kkgAAsIkAkEsQg0pT7bpRSpiTETCwgBAACsQiTqIoIhJgZG0QotS4Xi+VyaGYRrkmTiIpoWlFNoimLCDFZsLk5SFgARIRZrcNQa22tuRsBtMJMhBUmEAmAaoYAiAAOwByv/L3nsPGP/84fLYaymC3m89nlxfTy8qoO9Zvv/B1s/MbX/sWtxx/f3398d3cvSVosFh8ffvTjd99+650333rrjaOHhwEz1AgHLMKAAAVACELAHWbh5rVahMODmImImZiIkyQRFiEiJvJYIyImYiISIYCYk6SuzwQutbg5rjVry+XSzXCtmVlrzQwAgbDBTGsgYgKImFYAMAiAI9w83AOBawQiJmIWYhbJmlRzynl39+adJ59+4cUvffXll5/73HOj0ehT3zjGtbu/+8Qw+HJRZvP50cnJ/Xv3jo6Pp1dXrTVmFpGsiYjcvJZhOZ+XMpgZwoFwc7PmEVgLrES4u7mHOxExs67kPOr7ru/zBgA3q7W520ozc3cGsYhm7bpu1PeqSiwAIrzVVhbL2Wx6cXGhKd26dXt3b6/vOzObXl25edf3qrnLmYWambVWm7mZhyMCK0TMjJVAbbWWMgyl1FJrNTMW1q7rctf1neZMIkzs4QAzi7AkERZhERJJIqpJc97emuzv7+3sbI0nI80JKwQi/NSdVw6x8dFrt4hodnXBnJ75xhQbd799gBUCAQwgYLUNizK9uDg9eXh+/PDR8YP51YXXgd1Upc+p73MSCSAAiwgQiQQxiDTlvhullCkJMRMLCAGAQCzCJKoiAiFmxgYRSq3LxWK5HJpZhGvSJKIimlZUk2jKIkJMFmxuDhIWABFhVusw1Fpba+5GAK0wE2GFCUQCoJohACKAAzBHtTV3L60thrKYLebz2eXF9PLyqg6VWX7zrf8E1/7v//pkd3cvSVosFh8ffvTjd99+650333rrjaOHhwEz1AgHLMKAAAVACELAHWbh5rVahMODmImImZiIkyQRFiEiJvJYIyImYiISIYCYk6SuzwQutbg5rjVry+XSzXCtmVlrzQwAgbDBTGsgYgKImFYAMAiAI9w83AOBawQiJmIWYhbJmlRzynl39+adJ59+4cUvffXll5/73HOj0Sjl5A4QnvrLh7j2h/9LPjo5uX/v3tHx8fTqqrXGzCKSNRGRm9cyLOfzUgYzQzgQbm7WPAJrgZUIdzf3cCciZtaVnEd93/V93gDgZrU2d1tpZu7OIBbRrF3XjfpeVYkFQIS32spiOZtNLy4uNKVbt27v7u31fWdm06srN+/6XjV3ObNQM7PWajM383BEYIWImbESqK3WUoahlFpqrWbGwtp1Xe66vtOcSYSJPRxgZhGWJMIiLEIiSUQ1ac7bW5P9/b2dna3xZKQ5YYVAhDuvHGLj9Se+cvDNuwBo5eLkgUjqJqOUcgQ/+bUH2Lj32gEDBLRmbdnmV1fnx0fnp8eXpw/mVxdtWCKaMqnKKCuYmq3V1iwAYjATs+ZuPNrKuScmYiFhJgIzMYiFk6hmEYEQM+NabXUYhlpKaxbhRJxSyklzEklJRZOKSmIWB0pr5mASABbmrdahlFqsNTMjQJhImJmYmIiY2QPNLNxBDFAA5njl1z6PjX/yt/9oMZT5fDGfTS8vr87PLoZl+d9+8l9g43/89/7Z7VtP3Hzs8f3dfZY0n03vf3T/7XfefOedt999982j44cBCxjCAAs3YoAAIgQhKDyahZm3am4WcAYTAKaVJMJJkggR4xOBNSImYSaiiGCiru8BlFLMzCOw4WalFHdnZoCIKTzczcwDgZWIcMcGMYOIQMzExMQEEIBwD8QKACICwMwAAWBhBrFI1qSaJecbN/aevPPU8y9++eWXX37u858bj0ef+sYJrt39v27P520+X15eXT08Pr734d3j05P5fO7mmlVTylkJsNaGYbmYzWop7g6ACQivrbk7MyHCI9w+0cwACEtK0nVdPxpNxuOu67u+E+bWbMXDbaU1d8caqaak2uWsudMkkhIze7PlYjGbzqbTq5TSrVu3tyZbKUlt9epqGm6as6omESIBYG6lFDMDgYmEmUWYhQnmbq3VUmurpVYz8wgiTllz1q7rRLOIEFOsgUBELCIswiLEwkSSpM95sr21v79348b2aDJSVQDEYAIRVu68coiNj79zG8Dl6YlI+uxfG7Bx79sHARAAAgMEtGZt2eZXV+fHR+enx5enD+ZXF21YIpoyqcooK5iardXWLABiMBOz5m482sq5JyZiIWEmAjMxiIWTqGYRgRAz41ptdRiGWkprFuFEnFLKSXMSSUlFk4pKYhYHSmvmYBIAFuat1qGUWqw1MyNAmEiYmZiYiJjZA80s3EEMUADmsGbVw8NrbYuhzOeL+Wx6eXl1fnYxLIsQ/6/v/+e49tp/9XB/d58lzWfT+x/df/udN9955+13333z6PhhwAKGMMDCjRgggAhBCAqPZmHmrZqbBZzBBIBpJYlwkiRCxPhEYI2ISZiJKCKYqOt7AKUUM/MIbLhZKcXdmRkgYgoPdzPzQGAlItyxQcwgIhAzMTExAQQg3AOxAoCIADAzQABYmEEskjWpZsn5xo29J+889fyLX3755Zef+/znxuNRUnUHCE/9lUNc++e/7g+Pj+99ePf49GQ+n7u5ZtWUclYCrLVhWC5ms1qKuwNgAsJra+7OTIjwCLdPNDMAwpKSdF3Xj0aT8bjr+q7vhLk1W/FwW2nN3bFGqimpdjlr7jSJpMTM3my5WMyms+n0KqV069btrclWSlJbvbqahpvmrKpJhEgAmFspxcxAYCJhZhFmYYK5W2u11NpqqdXMPIKIU9actes60SwixBRrIBARiwiLsAixMJEk6XOebG/t7+/duLE9moxUFQAxmPDkq4fYeP2Jrxx88y4RAaCTjz7UnEdbW5o7gO987SE2Pvr2AQACzNyLLabTR2enj86OL0+OZ9OLupyiFuFgJhWJ8GUZSinLUswcxCuQ1PWjra2drh+LCIkkERYiYlJJKbFqTkmSQISZ3R1ARDSz1qq1ZmbuTsTMkpNoSpqUWTTJCotEYFmqWxAL3B1mrdVSW22tFYQzMQslERJhZiEC2NxKs3ALEMARsMCrv/EFbPyT//SPhqHMFovZ1fTy8vL09HyxGP7xn/yX2Pi7/+7/+8QTB7ceu7W/f5OJL6+u7t27++bbP/rxu2+/9/6PT06OAAtYoAEOOAHEABFIEAiDRVj1ZuZm4QaAsEbMssEiDCKm8HAENhjEwgCaGYO6vgMwLIdmhg1i8ma1VXdnkSRCoECYeYSHewDh4fAIrBCBwcREAIkQSIQBRIR7AGAmJiYmYiYiBgEgJiJWTSKimm/s3bxz5+kXX/zSv/PVl5/73Ge3tic/841TXPvwn96azcrl1ez8/NGDo+P79+6enp4ulksAOecurygBrdZhuVzMZ1YrVuj/ZwvuYzVPz7uwf6+X+75/v+e8zJk5Oztnzni9drL2vnhlp/Y2tRQlban6h4vWyCFQ1AqEo6pNZFVJC24KhQCFP1pQhEhLSGzUGDlOiS1sjCPoP1XV0lRUsZzaa3s3a493XnbmzMyZ8/ac5/V339d19TmPdxBIfD4Q5ghvtUU4EB7h9o7amptFBBGpppxT13X9Wuk6EXHzQLg5EO5u7ohwD2YiZhXNOXddyaVLqhExn82HxXyxXKrq5Z2dknMzG4ZhNpuZec6JmT2CiUTU3YdhaeYqojmllFPSJIkZ7mhutQ7WzFr1CKwwp6SSsqokVdbETADcwx0rzMJMYAEYgKqULm9tbe5e2dne2h5tjlJOoABATEwgwv6rB1i7/7VrAI7u30s5v/BJx9q9r+45CADjAgFm7oPNJ5PT46PT48Px48Pp5KwuJqiDcDBTEonwxbAchmExDGYO4hWIlq7f3Nwu3UhESERFWIiIKYmqckpZVVQgwszuDiAimllr1VozM3cnYmbJKkk1aWKWpLLCIhFYDNUtiAXuDrPW6lBbba0NCGdiFlIREmFmIQLY3IZm4RYggCNggWbW3MOimi2Xw3Q+n55PxuPx0dHJfL5kkn/wg1/EE1/9rw6uXNll4vH5+d27d177zjffeP07f/jmG48fPwIsYIEGOOAEEANEIEEgDBZh1ZuZm4UbAMIFYpY1FmEQMYWHI7DGIBYG0MwYVLoCYLlYNjOsEZM3q626O4uoCIECYeYRHu4BhIfDI7BCBAYTEwEkQiARBhAR7gGAmZiYmIiZiBgEgJiIOCUVkZTypcu7+/vv/uAHf+zf+yP/wfuff9/m1kZOOQIg3PjjB3jin/316YNHh2/fvXN0dDRfLADknEteSQS0WpeLxXw2tVqxQhDmCG+1RTgQHuH2jtqam0UEEammnFPXdf1a6ToRcfNAuDkQ7m7uiHAPZiJmFc05d13JpUuqETGfzYfFfLFcqurlnZ2SczMbhmE2m5l5zomZPYKJRNTdh2Fp5iqiOaWUU9IkiRnuaG61DtbMWvUIrDCnpJKyqiRV1sRMANzDHSvMwkxgARiAqpQub21t7l7Z2d7aHm2OUk6gAEBMz/yxA6x9fe+V/c/exRq9ffONXMrWpUul6wm6/4lHWLv3letYc4+oNp9Nz06Ox8eHZ0eH0/HZYjb2uoAbI4TJrM2HxfLC0MyJV5RESj/a3twuXS8inJRVRURVRJLknIrmlESVRcDs7kCYmQcQbh5u5m4eUKYkSeUCMxGzsoioA7W2cKxEwNys1Vqbt8HNPJyJWShpYhFhYQZA7lFbbWYRjEAAzfzjf/sDWPutn/vGsBym8/l0Oj07PTs6PJ7PF5+//Wms/ZV//3evX99/+urTu1eeAtHpyentu7e+/e1vvfH6d27e/P7xyaOAOwwwwJkCCGYCEUgQhAh3tObevJkhPDzCHQwCSESYRQQgYgoPIMIDADERc3iYNSbOJRPRcrFsZswEgIjMvNXqcGUhEQIFIszcw+ERCA9ccFxgAMQkxMTEzCDCGoGYaYVXRIQZABEBiAgiYpGkSZNcurS7f+PZD7z8wZ/8yZ96/oXnd3Z2fvRPn+CJW1+6OpksT8/OHz1+/ODBo3v37p2enCyHJYCSSym55ASgDsOwXC5m0zZUMCkzCwPRWnMzIFZ8zd7hHg5AmEW15FxWuq6UkjQR0wqAiAAQHkC4u7kjgphVtO+7ldJ1cMymkzoMZq5JR6MRsywXi2FYzhcLN2NmjzAzAuWSAbTWACRNJefSd2UlZxZBRDNrzaxV80A4ABLRlFhEmJhFUhIRIoShubmDCSACC8CAq0rXd1tbW1eu7Gxvb41Go5QTKAAQExOIsP/qAdbuf+0agHs/+H4u5UM/l7F2/6t7AXKAHT/kHlFtPpuenRyPjw/Pjg6n47PFbOx1ATdGCJNZmw+L5YWhmROvKImUfrS9uV26XkQ4KauKiKqIJMk5Fc0piSqLgNndgTAzDyDcPNzM3TygTEmSygVmImZlEVEHam3hWImAuVmrtTZvg5t5OBOzUNLEIsLCDIDco7bazCIYgQCaefMwX4lqNiyH6Xw+nU7PTs+ODo/n8wWIP3/rz+GJL//C27tXngLR6cnp7bu3vv3tb73x+ndu3vz+8cmjgDsMMMCZAghmAhFIEIQId7Tm3ryZITw8wh0MAkhEmEUEIGIKDyDCAwAxEXN4mDUmziUT0XKxbGbMBICIzLzV6nBlIRECBSLM3MPhEQgPXHBcYADEJMTExMwgwhqBmGmFV0SEGQARAYgIImKRpEmTXLq0u3/j2Q+8/MGf/Mmfev6F53d2drpRhxXCjZ8+wBNf/ktHDx48unfv3unJyXJYAii5lJJLTgDqMAzL5WI2bUMFkzKzMBCtNTcDYsXX7B3u4QCEWVRLzmWl60opSRMxrQCICADhAYS7mzsiiFlF+75bKV0Hx2w6qcNg5pp0NBoxy3KxGIblfLFwM2b2CDMjUC4ZQGsNQNJUci59V1ZyZhFENLPWzFo1D4QDIBFNiUWEiVkkJREhQhiamzuYACKwAAy4qnR9t7W1deXKzvb21mg0SjmBAgAxPfPHDrD29b1X9j97F2v0/W/9fun6y1d2u41N1nzjE4+xdvcr1xFYCQ80X86npydHZ8eHJ4cPJ6en88m4DjO3gc0AeNhyWNY61GbmLqIkSiyldKN+o+SOs6omTrqSUtKcU8655FyyqIoIwAG3iGaGcNUEoJmFWzNjoiQixMxChBUiUREiifAIuCPCmpm1ZnVoZtEswolXSFVYkggJCzEjsGzVzcLhAY9wxx/9lRex9oWf/8awHOaLxWQyPTs7Ozw8mk8Xn7/9aaz9pZ/6x/vXb1x96und3acAOjo6un3nrde+/a0333z9rVs3T0+P3S1gxA44UxCChEBMYIAiEAYLuDk8zM1aczc4wCCAmImYmVYiwj0CgQgQCbN7RDgRl64AaLU2MwKJMIE8vNZqbsJCuBBArS3C8a8jwhoTExEYDICYCCARYSYiXhFREWIGEB4AIhwAi6ispEs7l/f23v3iSy/9+Ec/+sILL+7t7b3ws+d44taXrk7OF0cnZw8ePHxw4eHp+KzWikDJaaXkBKDWulwslvO5tUogVlYRIFprbs4M9wACQER4hLsjwiMQASJmTppSTjnnpKmUkpKCiEDExERmHuG11mYW7iKSUi5d6fseHvPJtFpjZhFRVTebzxeL5aLVWltzd6uttkrMpRRVBSAiJefSdaN+1I/6vutUFYBZmNXamrsjAkQsknJiFncDi6ioXPCAV2vuWCEmEjDCPaU0GvXb21tXruxsbW91oy6pBkAMJhBhZf/VA6zd/9o1ADdf+4PS9T/+X1/G2v2v7sERIAcQWAkPNF/Op6cnR2fHhyeHDyenp/PJuA4zt4HNAHjYcljWOtRm5i6iJEospXSjfqPkjrOqJk66klLSnFPOueRcsqiKCMABt4hmhnDVBKCZhVszY6IkIsTMQoQVIlERIonwCLgjwpqZtWZ1aGbRLMKJV0hVWJIICQsxI7Bs1c3C4QGPcEczM0czq27DcpgvFpPJ9Ozs7PDwaD5dAPT5238eT3zpU7d3d58C6Ojo6Padt1779rfefPP1t27dPD09dreAETvgTEEIEgIxgQGKQBgs4ObwMDdrzd3gAIMAYiZiZlqJCPcIBCJAJMzuEeFEXLoCoNXazAgkwgTy8FqruQkL4UIAtbYIx7+OCGtMTERgMABiIoBEhJmIeEVERYgZQHgAiHAALKKyki7tXN7be/eLL7304x/96AsvvLi3t7extckCIux/4gBP/M6nDx5ceHg6Pqu1IlByWik5Aai1LheL5XxurRKIlVUEiNaamzPDPYAAEBEe4e6I8AhEgIiZk6aUU845aSqlpKQgIhAxMZGZR3ittZmFu4iklEtX+r6Hx3wyrdaYWURU1c3m88ViuWi11tbc3WqrrRJzKUVVAYhIybl03agf9aO+7zpVBWAWZrW25u6IABGLpJyYxd3AIioqFzzg1Zo7VoiJBIxwTymNRv329taVKztb21vdqEuqARCDCTc+foC1r++9sv/Zu1ij/+///t9Ho43dq1e3tne49M/8zAnW7n3lugcigAg0Xy5m49Ojs6PDo8NHk5Pj2eRsmE+tLdwaA25uVltYRIBIJDEphEm0aJGcVJJmXeGUkuaUc9fnVEoqXUrCIiByhLlba0SUUhZhMzcPt4qAMhMJLjg8wKIiKkIkANzdmjVr1mqtza2GRYQTgYhV5V9SVQ/U1swMAQ9YuHt87G+9iLXf/vlvLIdhPl/MprPT07PDw6PZdPb52/8N1n7po1/a39+/+vTe7pWrETg6Orz11lvf+e63vvf9P7xz5/Z4fGxuASN2wJmCABICEcAIwkpQOEWEe7i5WTWz8AAca8QMIqxEhHvgHcwMINxZNJdMRHUYzJ1AIrzi7nUY3IOYCAjA3NzMLbBGTACIsMbEBIAIKwwGwEwsqiLERMwqwiLCDMAj3DzCAbC8Y2vr0tWn3/W+5973oQ9/5KUXX3r3s+/5tz414IkffPGp8/H88PD47bfvHTx8dHT4aDydeGsAqWpSTUmBqCvDMMzm5g1EQqzKAFprQDATiAAwETFjLSLczN3NHYAws4iq5pz7vldVArGwihAzADNrtdVWzQxA0lS60nUdArPJxMxUlJgAtNpqHZbDUIehDnVZh1Zra42Yyw/lnFZyLqWM+lHfd6Xrc1IijvBm5h7uBgcYzMJMDtTWAlAVVU0pgcSbmTsiwLICIo9IKW9s9Nvbm5cvX97e3uj7okkdYMIKEVb2Xz3A2v2vXQPwzd/7P0ejjX/3l5/B2v2vPA0wQAF4IAKIQPPlYjY+PTo7Ojw6fDQ5OZ5Nzob51NrCrTHg5ma1hUUEiEQSk0KYRIsWyUkladYVTilpTjl3fU6lpNKlJCwCIkeYu7VGRCllETZz83CrCCgzkeCCwwMsKqIiRALA3a1Zs2at1trcalhEOBGIWFX+JVX1QG3NzBDwgIW7R23eLFpYa7Ychvl8MZvOTk/PDg+PZtOZB33hzqfxxG//3M3dK1cjcHR0eOutt77z3W997/t/eOfO7fH42NwCRuyAMwUBJAQigBGElaBwigj3cHOzambhATjWiBlEWIkI98A7mBlAuLNoLpmI6jCYO4FEeMXd6zC4BzEREIC5uZlbYI2YABBhjYkJABFWGAyAmVhURYiJmFWERYQZgEe4eYQDYHnH1talq0+/633Pve9DH/7ISy++9O5n33Nl9zInEqb9Txzgic996gcHDx8dHT4aTyfeGkCqmlRTUiDqyjAMs7l5A5EQqzKA1hoQzAQiAExEzFiLCDdzd3MHIMwsoqo5577vVZVALKwixAzAzFpttVUzA5A0la50XYfAbDIxMxUlJgCttlqH5TDUYahDXdah1dpaI+byQzmnlZxLKaN+1Pdd6fqclIgjvJm5h7vBAQazMJMDtbUAVEVVU0og8WbmjgiwrIDII1LKGxv99vbm5cuXt7c3+r5oUgeYsHLj4wdY+/reK/ufvYs1+r++9o82Nzf3ru9t7+yWze13/6lzrN3/yvUIeCAi0Hy5mJ2fHZ+ePD49PJycHk/OT5ezSV3OYI2JsEJBTCpCqioJLBGwQBiISVVZNalwWtGUu1RyKX3usiQVERAi0Nxqa8wy6ntRiYCb1WZwExasuJsH3ECSkxYfF1IAACAASURBVKomFgEoqjVrrdbaVoYw85UIBpiJRYSZRZJKShlEtTU3t2CEG6KZf+xvvoC1L/zc14ehzWfz2Xx+enp2eHg0mUy/cOeXsPaLH/6t/es3rj6999TuVQCPHx3euv3Wd19/7eYPvnf/4O3x2WmzGjDiYHJCEAMEECEIQQABBDAcKxHeqrmbh7lHhONfEb4SeIKZiJmIVSSXHB7LYWlmRMxMIuLudRjcg5gAhIfD3QL/JsREhBUGExPWmFlVmYWYhJhFWFiYAZi7m0c4ACJWVVHZ2Nje2bn67mff89LLL7/00svPP//8T3w64Ymb/+vu2dnkwYPDW7fvPHjw4OTkZD6fuzuIVISZk7J7mFsbhuVi7q2BWJlEGICZgZBEWZmYVYRFhBhM8DC3ZmatuTtWiEQkp1RKEVUATCxrqhIRzay1Zq15hIqmnEouQMxnM2vGzAB8DRG1tlqH+WIxn81qbQBYOKXU9/3mxmbXd0ycc8q5lK4rKWtWFQEQEe4IOAACB7y51drmi3m4S0op55KLanJ3OFZYRVJiFhCllEZ9v7W5eWX30sbmxmhUJAnWiPBD+68eYO3+164B+Oe/+9XNzc0/+isvY+3+V64CDDCACHggItB8uZidnx2fnjw+PTycnB5Pzk+Xs0ldzmCNibBCQUwqQqoqCSwRsEAYiElVWTWpcFrRlLtUcil97rIkFREQItDcamvMMup7UYmAm9VmcBMWrLibB9xAkpOqJhYBKKo1a63W2laGMPOVCAaYiUWEmUWSSkoZRLU1N7dghBuimddqzaKZVavD0Oaz+Ww+Pz09Ozw8mkym4fjtt38JT/zmJ19/avcqgMePDm/dfuu7r7928wffu3/w9vjstFkNGHEwOSGIAQKIEIQggAACGI6VCG/V3M3D3CPC8a8IXwk8wUzETMQqkksOj+WwNDMiZiYRcfc6DO5BTADCw+FugX8TYiLCCoOJCWvMrKrMQkxCzCIsLMwAzN3NIxwAEauqqGxsbO/sXH33s+956eWXX3rp5eeff35v71rqkwrvf+IAT/zaJ7/94MGDk5OT+Xzu7iBSEWZOyu5hbm0Ylou5twZiZRJhAGYGQhJlZWJWERYRYjDBw9yambXm7lghEpGcUilFVAEwsaypSkQ0s9aateYRKppyKrkAMZ/NrBkzA/A1RNTaah3mi8V8Nqu1AWDhlFLf95sbm13fMXHOKedSuq6krFlVBEBEuCPgAAgc8OZWa5sv5uEuKaWcSy6qyd3hWGEVSYlZQJRSGvX91ubmld1LG5sbo1GRJFgjwv6rB1j7+t4r1z9zh4gA0D/9rb+/ubl945l3Xbl6bWNr59k/vcDa/a9cD8AD4QGP5Xw2GZ+MT45Ojw7HJ8fnZ8fz8/GwnHqrQsTCSUWTlpw1ZdYEkJlXc2sGgFRVLrCIppRyzqVo6XLJmjILgRjh1azVxiqj0aiUDLCZ1Tq4u4ABmJubhTkRp5xVRUURaM28tVqH2sxbdTf3AMAEZmJiUWEmUUmaQVybhZuDELCIZvaxv/Ui1n7rP/v9YRgWy8VkMjsdjx8fHk2nsy/c/W+x9vMf+F/2r+9fe/raU09dA/D48eGt27def/3bb926+fDhg/H5iVk1b8TB5IQgBggrEQQngAgEFoBWEAj3CG9m4ebuZg4gEFiJcHc8wcxEnJKyiIpG+GKxaLUSM4hUJCJqreFBTADCAwAxERC4EB4OBxABIqwwmJgIIGYQCbOIsAiDWJiYhZmIAJi7m0c4ACJOSUVSPxptb+1e37/x3Pvf/+JLL3/ogx/8I395C0+88fmd4+Oze/cf3Lz51oODg/H4bDkMTMTMIBImZgbCzazW5XIZZkQkzKrMQDMDUcpJVVguJFEWEZWIcLPaWq3VzTyCiVhE+AKI3J2ZRSStqYh7eLiZARARVS25IGK5WLTWIgIR7g6ipCkQy8ViOpuNx+NWq6akqkm1H40u7+yUrkMEM4uoqKSUVUVVhRkAEQEgYmKqrc3m89lsOplO3UxSKqUbbYxSyggGIEyiKZfCmoQpacpd3t7avHx5Z3Nzox9lTRK4QIQf2n/1AGv3v3YNwD/7wuc2N7f/47//E1i7/492wQowgAA8EB7wWM5nk/HJ+OTo9OhwfHJ8fnY8Px8Py6m3KkQsnFQ0aclZU2ZNAJl5NbdmAEhV5QKLaEop51yKli6XrCmzEIgRXs1abawyGo1KyQCbWa2DuwsYgLm5WZgTccpZVVQUgdbMW6t1qM28VXdzDwBMYCYmFhVmEpWkGcS1Wbg5CAGLaGa1Wq3WwupgwzAslovJZHY6Hj8+PJpOZ6357xz8RTzxmf/kD5566hqAx48Pb92+9frr337r1s2HDx+Mz0/MqnkjDiYnBDFAWIkgOAFEILAAtIJAuEd4Mws3dzdzAIHASoS74wlmJuKUlEVUNMIXi0WrlZhBpCIRUWsND2ICEB4AiImAwIXwcDiACBBhhcHERAAxg0iYRYRFGMTCxCzMRATA3N08wgEQcUoqkvrRaHtr9/r+jefe//4XX3r5Qx/84P4z7xqNiiS58YkDPPE//My/eHBwMB6fLYeBiZgZRMLEzEC4mdW6XC7DjIiEWZUZaGYgSjmpCsuFJMoiohIRblZbq7W6mUcwEYsIXwCRuzOziKQ1FXEPDzczACKiqiUXRCwXi9ZaRCDC3UGUNAViuVhMZ7PxeNxq1ZRUNan2o9HlnZ3SdYhgZhEVlZSyqqiqMAMgIgBETEy1tdl8PptNJ9Opm0lKpXSjjVFKGcEAhEk05VJYkzAlTbnL21ubly/vbG5u9KOsSQIXiLD/6gHWvr73yvXP3CEiAPTFv/sr25cuPfPu91zdu751ZfdH/qxh7f5XrgMIAA73GBbzydnp+PTo5PHh+Pjx+Oxocn66nE6sDkKkSXJOfSld36WciTUihqGZr4CZISJ8gURURHNJOaeUU5c1ZWICwSLcbDkMrLK9tZVKR0RuNtTmZkxAwM3M3c2YuOSsokQcDmu1tpXBm5lbmAMghjATS2JiYWFhJmIxwFrzABN5wDys2X/0t1/G2uc++f8My2G+WEyms/HZ+dHx8WQy+4f3/zus/exzf+/69f1r1/auXb0G0OPHh3fu3H7jje/evvPW4eGD88m41mXzShxMTghiBAJAOAACiMAEIREiEgJDAtG8uVkzc3MgwiMQK+aOJ4RZRFJKLCLMrbXpdNZqdTiDRSXcm1sEVISAAJg5aSKm8PBwcw8z93A4AAYTEwEkIsxExCsiDGJhgFhYmAkIoJmFu5sTE4skEUm5z/1oa+fpp/eefe97X3rpAx/+yCsf++u7eOKbn904Ojq6c/fem29+78H9g/PJpFlLKTEzVoiEiQF3D2ttqB4uzCKcRIBwdxBpUlFhZtELSZVFALhZba3W6mZYIRKRiBiWy1qruwNQ1ZRz33Up5ZSUmT2CiVk4pVRyBtAWQ221mYVf0JQ2NzeFZTafTc7PT09Pa2t936eURGQ0Gl25fDmXztbcjYmIWVVTSipCJKpCTMJMLPPF/OTk+OT0bDKdtNY0pa7vt7e2SumYhJhVJOXc9aOUM4uoppJlY7S5c+XS1tbGaFQkCQIgrBBhZf/VA6zd/9o1AF/6tb+zfenSJ//hx7B2/4s70AwSrAUAh3sMi/nk7HR8enTy+HB8/Hh8djQ5P11OJ1YHIdIkOae+lK7vUs7EGhHD0MxXwMwQEb5AIiqiuaScU8qpy5oyMYFgEW62HAZW2d7aSqUjIjcbanMzJiDgZubuZkxcclZRIg6HtVrbyuDNzC3MARBDmIklMbGwsDATsRhgrXmAiTxgHtZs0aw2a2a11mE5zBeLyXQ2Pjs/Oj6eTGZDtS8f/jKe+NWf+X+vXb0G0OPHh3fu3H7jje/evvPW4eGD88m41mXzShxMTghiBAJAOAACiMAEIREiEgJDAtG8uVkzc3MgwiMQK+aOJ4RZRFJKLCLMrbXpdNZqdTiDRSXcm1sEVISAAJg5aSKm8PBwcw8z93A4AAYTEwEkIsxExCsiDGJhgFhYmAkIoJmFu5sTE4skEUm5z/1oa+fpp/eefe97X3rpAx/+yCvvefbZ0WbPKs/8zAM88Rf/w//twf2D88mkWUspMTNWiISJAXcPa22oHi7MIpxEgHB3EGlSUWFm0QtJlUUAuFltrdbqZlghEpGIGJbLWqu7A1DVlHPfdSnllJSZPYKJWTilVHIG0BZDbbWZhV/QlDY3N4VlNp9Nzs9PT09ra33fp5REZDQaXbl8OZfO1tyNiYhZVVNKKkIkqkJMwkws88X85OT45PRsMp201jSlru+3t7ZK6ZiEmFUk5dz1o5Qzi6imkmVjtLlz5dLW1sZoVCQJAiCs3Pj4Ada+vvfK/mfvYo0+9z/+1e2trev7z1y7ceOpvRvP/3zC2r2vXAOIQAiEx3K+mE3GZydHR4cPT48enRwdziani+kkWkvKSbUU1XRBVJnYA9bMAeUkqqwqIgAxE6kIK2lKWUvXSc7EAsaKmw3DQCKbm5ulFBC723IYwoxACAARDndnYlERIoDhbq0NdRgWQ62L5dDcjQBVSUmLJk0iJGAwkRMjUM0QAAGgAJrFx3/1Q1j7zT/ze8vlYj5fnE+mp+fnpyenk8nsiw/+MtY++SP/8971/b29/b2n94jl+Oj4zt1br7/xndtv/eDR44fn5ye1Dh4N5AQHHAggAnAQggAwmEgAIhALJ1UijnB3M3c3a2ZuDkR4YI2YcIFYOKUkzERUa1ss5nUYAheYGYC7A2ARAgVCRXLKLNzM3KzWFghEhDsAYgaRMBMRMwPEwsJMRAAR0woTYa01i3BzZ5AkTSIp5ZL70ebO7u7VG++68f4XXnzllR//6V95F574g1/vHz56dOv27TfeePPB/YPz6cTMSs6iSYSFWZiw4m5u0QwIYhYmZmKgmYGIhVWFiFVFVJkZKxG1tVabuSECRMKsqq21yWSyWCybGRFyzn3XjUaj0nUll5RUVJmYhVUkpQQwvLWhLYfB3ADkUnYubTPLbDY7n5yfnY3dbGNjJJoQ0fX97pXdrsutWTOzVs2DAGZhJhZJIixCzCqSUp4v5o8OD0/PTmezWWtNUypdt7W11ZVOJDHLSl7p+5KyJM05lZw3N0c7O5c2tjb6PqtKBEAgwg/tv3qAtftfuwbgH/zNv7G9tfWpf/qnsHbvtzcpd2ANCoAIhEB4LOeL2WR8dnJ0dPjw9OjRydHhbHK6mE6itaScVEtRTRdElYk9YM0cUE6iyqoiAhAzkYqwkqaUtXSd5EwsYKy42TAMJLK5uVlKAbG7LYchzAiEABDhcHcmFhUhAhju1tpQh2Ex1LpYDs3dCFCVlLRo0iRCAgYTOTEC1QwBEAAKoFnUZtWsmdXBlsvFfL44n0xPz89PT04nk9kwtH98/NfwxN/54/9i7+k9Yjk+Or5z99brb3zn9ls/ePT44fn5Sa2DRwM5wQEHAogAHIQgAAwmEoAIxMJJlYgj3N3M3c2amZsDER5YIyZcIBZOKQkzEdXaFot5HYbABWYG4O4AWIRAgVCRnDILNzM3q7UFAhHhDoCYQSTMRMTMALGwMBMRQMS0wkRYa80i3NwZJEmTSEq55H60ubO7e/XGu268/4UXX3nlx9/7o++9tLOVUn7mTzzAE7/4E19+cP/gfDoxs5KzaBJhYRYmrLibWzQDgpiFiZkYaGYgYmFVIWJVEVVmxkpEba3VZm6IAJEwq2prbTKZLBbLZkaEnHPfdaPRqHRdySUlFVUmZmEVSSkBDG9taMthMDcAuZSdS9vMMpvNzifnZ2djN9vYGIkmRHR9v3tlt+tya9bMrFXzIIBZmIlFkgiLELOKpJTni/mjw8PTs9PZbNZa05RK121tbXWlE0nMspJX+r6kLElzTiXnzc3Rzs6lja2Nvs+qEgEQiLD/6gHWvr73yvXP3CEiAPTrv/zpru93d5/au/GuZ3/k/R/69GWs3fnSUyASEQDeoi6GYTE9Ozl6eP/tx48eHB89nJ2ftsWcEaNcUhYQAW5m7g4wQCwimvquL6XLXRFRQxBBWMGo5prSaGMzd4VEWISYERiGgYhK32dNYJj7MAytGQJMLCJMxGDA3QMAAwhvzYbF8nwymZ6Pz8/Pl8uBGX0pW1ub/agvpRMmM8MKMwLNLACQEBOIAfrpX3sFa7/5Z/75fLGcTWfjyXR8fn52Np5MZl96+MtY+9nn/t7+yt7+3vUbKeWz07O7b9967bVv3rz5/fsHd8fjE3PzaEALt4ABDsABBgPkQXAEKCwCwSIl56TKIgyY28owDM0s3AGwiDATMRArAFiEiGLFvbbmzQIBgEDEhHcQMQFQEVEF0IbarJmZezDTCjMDBICYVhjEwgARE4DwwBPEFB5AuLmFE5GK5qQp5a7bGG1s7+zsXn362nPve+7HPvzKn/3Mi3jim58Z3bt3/+Zbt998/Y17BweT86l5TSmXkruuz0l5BdHM4A4EACbCSjjCL0QEBTGrqjADZG6t1mGtmQEQFk2a9EKt9fz8fDafWzNNOhptbG6M+tGo7/sul9J3pRQVAUBEIGLipGLN58uFWSPiUvLm1hYTz+bz+Ww6m80AdF3HoGUdSiqXd69s9D2Y3a02czME3K22CiBpYiaPyCmPRqPa2tHR4/PJZBgGRJBISrnv+1IySxYWJtKUJOeSc8q578potLG1Nbq0vT3a7FNRFcYK4Yci4sbHH2Dt3j95moh+46/8ha7v/8Lv/RdYu/O5LN0GkpoHiEQEgLeoi2FYTM9Ojh7ef/vxowfHRw9n56dtMWfEKJeUBUSAm5m7AwwQi4imvutL6XJXRNQQRBBWMKq5pjTa2MxdIREWIWYEhmEgotL3WRMY5j4MQ2uGABOLCBMxGHD3AMAAwluzYbE8n0ym5+Pz8/PlcmBGX8rW1mY/6kvphMnMsMKMQDMLACTEBGKAqoe5u0Wtdb5Yzqaz8WQ6Pj8/OxtPJrP5Yvjds7+BJ37tT/7+3vUbKeWz07O7b9967bVv3rz5/fsHd8fjE3PzaEALt4ABDsABBgPkQXAEKCwCwSIl56TKIgyY28owDM0s3AGwiDATMRArAFiEiGLFvbbmzQIBgEDEhHcQMQFQEVEF0IbarJmZezDTCjMDBICYVhjEwgARE4DwwBPEFB5AuLmFE5GK5qQp5a7bGG1s7+zsXn362nPve+7HPvzK+5770aevXR2NRjf+xAGe+C//7d+5d3AwOZ+a15RyKbnr+pyUVxDNDO5AAGAirIQj/EJEUBCzqgozQObWah3WmhkAYdGkSS/UWs/Pz2fzuTXTpKPRxubGqB+N+r7vcil9V0pREQBEBCImTirWfL5cmDUiLiVvbm0x8Ww+n8+ms9kMQNd1DFrWoaRyeffKRt+D2d1qMzdDwN1qqwCSJmbyiJzyaDSqrR0dPT6fTIZhQASJpJT7vi8ls2RhYSJNSXIuOaec+66MRhtbW6NL29ujzT4VVWGsEFb2Xz3A2tf3Xrn2G7eZ1n71z38qlbK9s7N3/Znnnn/xo3/tGazd+eJTIBIVAFa9LYdhOT87OT54+87hw/tnJ4fzybgNCwnPmlTg4ebWakWAWVg0reRccle6LpciIoYgIkkJQG3GqpsbG6krYGER0QSgNiNCV4qIgNjClsPgzcLBLDmprBBHoLbqZggLDzMb5ovJ+WQyOR+Px8vlgom6kre3t/rRRleKqrRmQACwgJk5wCzEwiIB+pO/8e9g7bP/6f8xmy0mk+n4fDI+n6xMZ4svH/5VrP3nz//G3t6N/ev7+9dvpFLG4/O7t9/6g2994/vff/P+/bvj8UnAPMzDEM29eTgoAEIQQOEUQFiEw8KVuJSSkqomZgLQrC2XQ7Pm5sSkIiwizB7hZgClpAAiwtzD3c0dgTUGEdMK3kEsrMweUS80s+YeIrxCzEKMNWJaAYiYAIRHhAMID2JaARArHo4QZlFNKeWUS+n70aXtre3tS5effc+zH/qxj/zCFz+KJ77xd/OtO3d/8IO33nzzzQcPHk5nc4TnnLu+39zYKCWLMCLMDOGMCx4BN2vNzTzcw80dBGYmInO31uaLxXJlsTAzAJpS3/VdV7quAzBfWSxabcS0ubHRr5XuQt/1o1Gvqu4BhEcwEYu62TAMZsZMqtp1HYDZfL5cLGurIrIxGhHRcjmIyPalS33fMxGA2pq7IVBbXS6WQLAoEaxZKXl7+5K7j8/PF/NZMwNAIipaSk4pi6iwEDGTcNaUUi6l77uNjc2trdH29lbfd6mICoPwQxEB4MbHH2Dt3j95moj+p0//Qirlv//mn8PazV/3tLUtuVgQiEQFgFVvy2FYzs9Ojg/evnP48P7ZyeF8Mm7DQsKzJhV4uLm1WhFgFhZNKzmX3JWuy6WI/P9UwQuwZWdWH/b/Wut77L3POfd26923W9LMaHjNIJCEhLHBDwwxCZQmuAKGZELKEAenCrvKsTGPYCeEEDDPGE8gaDKFCUU5TmIbZyg7pFyVKpPChqCRhpgkDtNSqzVILan73nve++z9fWutnHuka8a/nyiciCRGAKUqhzCdTGKTwcIiEiKAUpUITc4iAmJ1HcbRqrqBWVIMskfsjlKLqcLVzVV17Hfr1Xq9Xi2Xy2HYMVGT09HRrO0mTc4hSK0KOAB1qKoBzEIsLOIgc68GUx3Gcbvdrdeb5Wq9XK33Ntvdbjf+49WP4NLPf+tLJ9eux5yXy9Vnb996+f966ebN33vzzc8ul+cONVdzhVezam4gBwhOALmRA67uBnULxDnnGEMIkZkAVK3DMFatpkZMQYRFhNncTRWgGAMAd1czNzM1g+OAQcS0h/cQCwdmcy8Xqmo1cxHeI2YhxgEx7QFETADc3N0AuDkx7QHwPXODC7OEEGNMMeXctt3x0ezo6Pjq4+97/Euf+rIv+IIveOzR69Oj2WPf+jYufeeX/tJbb7292fZwSyk1bTudTHJOIgx3VYUb44K5w1RrNVVzMzc1A4GZiUjNtNZ+txv2djtVBRBibJu2aXLTNAD6vd2ulkpM08mkPcjNhbZpu64NIZg54ObORCzBVMdxVFVmCiE0TQNg2/fDbii1iMik64hoGEYROTo+btuWiQCUWs0UjlLLsBsAZwlE0Ko5p6OjYzNbrla7fltVAZBIkJBzijGJBGEhYibhFGKMKee2bSaT6WzWHR3N2raJWYIwCO86ef4ODl585NmHX7jNdPAT3/UduWmOrlx5+OTGBz74BX/iJ74QB2/8vQfBQkTurtXKMAz95vzs7O03Xz+/d3eznu+2q7LrbRjcqmpVLQACS4ypadqYUogxsBCzsFAIxAwgBIkpQQIAlpiaxDEAzCIsAiK4k0hKSYKASM1LGa0qHESSUooiTOTuqlrKWMax1gJzLToMfb/d7Ya+jAWwGCTnpm1ySjnESAR31DIWveAgkihCxOLE3/LCV+DgZ7/p1zbb7XK1WcyXy/W674dhGP+X8x/GwZ//wk9cOzk5uXb92rXrIYTlYn7rtVuf/vSnPnPzM2+//eZ6tXCoQ9Wre3WtDjM43N3IQXC4wQ0OmLsQhRBDCDEKMwMws1KqqaobEQURYiYi3zNn4ZyzMNWq7q57Zm4OOEDEtIfPwUQiYmZlr1at1d2JiJkBYmFcYhAxAVAz3zMHHCBiEmJiwiUWSXEvpNSk1ObU5dzlJl+/8ejTTz391//J1+PS//Hj9ZVbt1599dVbt167d++01kokXdd0e5NJm3MUAcNUzQxublZr1bHUOpZazc3dLrhVM1M181LGsZRhN/R9X2tR9ZTTbDY9Pjo+vnLcti0Raa3rzcbNQowpXsgHTdNOptMYxMzUHG57pVYzg7uZOxwAgVRrv+2LVibKTXN8dBRiMlVmiinFEInI4bWquxGxqg7Dzs2IGe61akppNpuFGMs4ljLaHsDMEkKMMcXELETCBGIhkRhTTKltUtu0k1l3dDRr2yZmCcIg/Cvufv0jb+HgjU8+REQ/+Re+MzfNj/5/P4CD/+e/3kyu3he7ibOAhYjcXauVYRj6zfnZ2dtvvn5+7+5mPd9tV2XX2zC4VdWqWgAElhhT07QxpRBjYCFmYaEQiBlACBJTggQALDE1iWMAmEVYBERwJ5GUkgQBkZqXMlpVOIgkpRRFmMjdVbWUsYxjrQXmWnQY+n672w19GQtgMUjOTdvklHKIkQjuqGUsesFBJFGEiMWJVa2qlzLudsNmu12uNov5crle9/0wDGMp+o/XP4pLH//o71y7dj2EsFzMb71269Of/tRnbn7m7bffXK8WDnWoenWvrtVhBoe7GzkIDje4wQFzF6IQYgghRmFmAGZWSjVVdSOiIELMROR75iyccxamWtXddc/MzQEHiJj28DmYSETMrOzVqrW6OxExM0AsjEsMIiYAauZ75oADRExCTEy4xCIp7oWUmpTanLqcu9zk6zceffqpp7/wwx/+4BNPHF05fuLbTnHp2z7/hXv3TmutRNJ1Tbc3mbQ5RxEwTNXM4OZmtVYdS61jqdXc3O2CWzUzVTMvZRxLGXZD3/e1FlVPOc1m0+Oj4+Mrx23bEpHWut5s3CzEmOKFfNA07WQ6jUHMTM3htldqNTO4m7nDARBItfbbvmhlotw0x0dHISZTZaaYUgyRiBxeq7obEavqMOzcjJjhXqumlGazWYixjGMpo+0BzCwhxBhTTMxCJEwgFhKJMcWU2ia1TTuZdUdHs7ZtYpYgDMK7Tp6/g4MXH3n24RduMx388H/47+amObpy5aFHTh5/3+d9wwvP4eCNv/cgSQDg7qY+7nb9dj0/u/fOW28uzk+HfjVsN0O/KdvtOO5qGWqpwmhz07Zt101iyiICEOBqcLyLYwoxpxATSQxBKAiLEAuxSBBicYBFckosAkI1K6W6KjmIJcUkQYQI7qpayjj2u6rV9UKpqmNRq3twJaIgEmOIKcW9ENy8H8ZSJSMDQgAAIABJREFURlV1EIkICwk76Fs+/kdw8DPf+Kvr9Wa5WJ8vFsvVZrvtx6K/tv1xHPy5z3/h5Nq1Rx45Obl2XUKcn5/duvXqSy9/6uYrv3fv9O1+u3aoQd3NvcLVTA3mDjfA4QYHwcgBc2ciAXPgEISYiQiAqfqeOQAWBoiYALh5jKFp2iBStVZV0wvmDoCJADARLjlAADGbWdmr1ao6HAAzA0RMOCAiAAwCoG5uDjguEDHtMQgAMe2xSIwxxZhiTqkJsQmSWML1k5MvffqZH/tn34xLv/aDy1du3rx167Xbn/3sfL4wM5E4mXZ7k8kk5xSFmcjUzA1mamq1ljLWsbgpCAbYhTrWPd2rqrWWcRz7vh+GoZQSQ5xOp0fHx/ffd7WbTEIItdb1eq2qMVyIKeWcm6Zp23Y6mUiIemCq5uZmAIioVi21aK2qOpYyjqOZiUjbNLPZUUoJADOBmAhMpOa1jABCTETQWs2diQEvtcYQptNZTMlNVc3dzQ3MIhIkhBCJhXGBWChIDCmm1OTUte1kNjk6mrZtE7MEYRD+FXe//pG3cPDGJx8iov/qz31bbpqfvv3DOPg/f+jO1Ycebo6ucMokAYC7m/q42/Xb9fzs3jtvvbk4Px361bDdDP2mbLfjuKtlqKUKo81N27ZdN4kpiwhAgKvB8S6OKcScQkwkMQShICxCLMQiQYjFARbJKbEICNWslOqq5CCWFJMEESK4q2op49jvqlbXC6WqjkWt7sGViIJIjCGmFPdCcPN+GEsZVdVBJCIsJOygUr1ULePY7/r1erNcrM8Xi+Vqs932Y1E1/JPdj+PSz33LiyfXrkuI8/OzW7defenlT9185ffunb7db9cONai7uVe4mqnB3OEGONzgIBg5YO5MJGAOHIIQMxEBMFXfMwfAwgAREwA3jzE0TRtEqtaqanrB3AEwEQAmwiUHCCBmMyt7tVpVhwNgZoCICQdEBIBBANTNzQHHBSKmPQYBIKY9FokxphhTzCk1ITZBEku4fnLypU8/8+STT37+F33R8ZUrX/Bnz3Hpmx/7mfl8YWYicTLt9iaTSc4pCjORqZkbzNTUai1lrGNxUxAMsAt1rHu6V1VrLeM49n0/DEMpJYY4nU6Pjo/vv+9qN5mEEGqt6/VaVWO4EFPKOTdN07btdDKREPXAVM3NzQAQUa1aatFaVXUsZRxHMxORtmlms6OUEgBmAjERmEjNaxkBhJiIoLWaOxMDXmqNIUyns5iSm6qau5sbmEUkSAghEgvjArFQkBhSTKnJqWvbyWxydDRt2yZmCcIgvOvk+Ts4ePGRZx9+4TbTwX/+731jbprplSsPPPjwIzce/+j/+HU4+Oz/dL/EhANXH/rdZrNczs9O772zWp4N2/Vusx6269Jvhr4vZTSrQaTL7aRrm7aVEN2x546qWkpVNyKOIcSUYsqhaSUICMQSUgohhSRgUTNhjjGRCIjcvJTi5gQwS4pJRJjJ3F211rGUsZZipVZTLeow7LnDDb4HFqQQ0l5OruiHYRhHVTWASYgJYAe+9RNfiYOfev4fbtab+XI1ny/mi9VqtR5G/XX/GA6+7bGfefjhhx955OTk+mNR5PT07Natmy+9/OKtV2+ezU93Y0/kIHNXQF2rw9TVDe5wwwUjgAEiJjffI4CFWViYiQgH7g6AiADCpRRDO5kEkaqqtZSqpuruAIiImUFEuOAA3AG4WTXTWquqVXU4gYgJIGLawyUGERMO3NzgOGAQAIMziJhYJIrEmEJMKTUxtSIRjmvXrj/zzDM//eJHcekf/NU7v/eZz7x2+/adN9+aL5fuHmOaTrq269qmiSkKEwPme0Z+wVShaqZMJDEws9peLbVWVXN3uzCM467vt30/DqO55ZS6bjKbTVPOcB/HcbPtYZabJje5a9u2O2i7yaRjkVqr1lpqFebctDGIOcZht1qvd/2ualVVuJs7E8UQ266NMWKPiEAONzXVOgwjC0+n05ySiDCziJhjHEdhartJDALAHKpqZgBIWFiIiMEAYU+YJcS9lLq2nUynx0eT6WzWtjlmCcIgfK6T5+/g4I1PPkREP/jRb8pN87F3fgoH//t/8v8+dOPG7L4HeDKVmHDg6kO/22yWy/nZ6b13VsuzYbvebdbDdl36zdD3pYxmNYh0uZ10bdO2EqI79txRVUup6kbEMYSYUkw5NK0EAYFYQkohpJAELGomzDEmEgGRm5dS3JwAZkkxiQgzmbur1jqWMtZSrNRqqkUdhj13uMH3wIIUQtrLyRX9MAzjqKoGMAkxAexAqToWHcah3+026818uZrPF/PFarVaD6MSyT+1v4lLP/n8r59cfyyKnJ6e3bp186WXX7z16s2z+elu7IkcZO4KqGt1mLq6wR1uuGAEMEDE5OZ7BLAwCwszEeHA3QEQEUC4lGJoJ5MgUlW1llLVVN0dABExM4gIFxyAOwA3q2Zaa1W1qg4nEDEBREx7uMQgYsKBmxscBwwCYHAGEROLRJEYU4gppSamViTCce3a9WeeeebJp5768Bd/8dHx8Rd++xyX/u2Hf2K+XLp7jGk66dqua5smpihMDJjvGfkFU4WqmTKRxMDManu11FpVzd3twjCOu77f9v04jOaWU+q6yWw2TTnDfRzHzbaHWW6a3OSubdvuoO0mk45Faq1aa6lVmHPTxiDmGIfdar3e9buqVVXhbu5MFENsuzbGiD0iAjnc1FTrMIwsPJ1Oc0oiwswiYo5xHIWp7SYxCABzqKqZASBhYSEiBgOEPWGWEPdS6tp2Mp0eH02ms1nb5pglCIPwrpPn7+DgxUeeffiF20wHP/BNX59yMz0+vnr//fc/dPIf/2/fgoPX/85xyC2IALj7MAyb5WIxPzs7u7tazHfrxbBZDZt1Gfoy7EyLm4lIl5vc5DY3EkQdAAFUte6G0VRBwswiHFJu2pZjNDcOMTdtzjnkCJJaFUBMgSWCyNxrVTcjcGCJMbAIE5ubaVVVuFotpdSqClUQAgsYMLjrHmDCHIOklBy0G8cy1qrqZiB2EAjV/N//hT+Gg5/8hl9ZrTer9Xo+X5zPF+fnq2Ec/3l4AQff9ODfeODBh04eOXn0xuMxpdPTe6+8evOlT/327ddfW67Ox9ITADbAAHVTh6qaw02x50bETBBiJiJX01INxkxMTMwsLMxEhD9AOCCmFGPTtsJUVbVoKaO5mzsA4QsgYmYAbuYA3FW1VjXVqupmDicQMREzEQFgEA6I6QIzADcHfA8Hbm5wAAxikRBEQkghxpBTM2GJAJ88cvLsc8/+9IsfxaVf/q5XP3PzM5+9/fpb79xdrlalagzSddOua3LOKUYWYsDcGSDsOdzhzgRhyW1mEbirm5mqvwfupZRhGPrdbuh3RSscIhJiYOJaylDGYTcw02QymU6n3WQynU67vbZr2laYyl6tphpC6LpOQjDzXb9dLpf9bufuRBQkAF5qhbuEC8IXiNnNzXQYx2EYQghXr1zpJpOckkggZjcby0jEbZNDiCDAUVXdTd0BEBETwwEQAGLmmFJMMYaua49mR7OjyXQ2a9scswRhED7XyfN3cPDGJx8ior/2zR9Jufn5xcdw8A++45/deOx9Vx++lo+vhNyCCIC7D8OwWS4W87Ozs7urxXy3Xgyb1bBZl6Evw860uJmIdLnJTW5zI0HUARBAVetuGE0VJMwswiHlpm05RnPjEHPT5pxDjiCpVQHEFFgiiMy9VnUzAgeWGAOLMLG5mVZVhavVUkqtqlAFIbCAAYO77gEmzDFISslBu3EsY62qbgZiB4FQzUvVMupQx6EfVuvNar2ezxfn88X5+WoYR4b8Rvg5XPqhP/lrj954PKZ0enrvlVdvvvSp3779+mvL1flYegLABhigbupQVXO4KfbciJgJQsxE5GpaqsGYiYmJmYWFmYjwBwgHxJRibNpWmKqqFi1lNHdzByB8AUTMDMDNHIC7qtaqplpV3czhBCImYiYiAAzCATFdYAbg5oDv4cDNDQ6AQSwSgkgIKcQYcmomLBHgk0dOnn3u2S956qknv+TJyXT6hd+xxKWvv+9HlqtVqRqDdN2065qcc4qRhRgwdwYIew53uDNBWHKbWQTu6mam6u+BeyllGIZ+txv6XdEKh4iEGJi4ljKUcdgNzDSZTKbTaTeZTKfTbq/tmrYVprJXq6mGELqukxDMfNdvl8tlv9u5OxEFCYCXWuEu4YLwBWJ2czMdxnEYhhDC1StXuskkpyQSiNnNxjIScdvkECIIcFRVd1N3AETExHAABICYOaYUU4yh69qj2dHsaDKdzdo2xyxBGIR3nTx/BwcvPvLstY+/DoD2/tNv/FMx5+nsaHrlyvTKA9/zm9+Jg1d/sWu6KYjBBMI4Dqvlcn5+dvbOW+fn97bL+XazLNutlQFW4QZzZkohpBRzSiEEYgaxsKhavxtUjZkNXtVYJLcth2DuMcXJdNa0k9gkEA1jBSzEKCJEog5ThYEA2QtBmAGYw2oBnIXgXsdRVeFGxDEGYSaC7tVSa4UriIIIiN2hqqWWqu4OcxhgVf+DX/pqHPz4N/zKtu/Xq81yuTw/n9+9N+/7/rfyJ3Dwb3V/7YEHHzq5dv3xx9+fUz49O331lVde/vSLt2/f3varUnqQAwaoQwEzVzPzPYMbERNBSEJgYWJTHcexqoKcQEGEgwgxCwOEC473EDFFkdw0xKRFq9ZSq6nigEWELxARDhxw1aJqqmpmam6GA2JmYQYREwAiwgViYWEmwAE18z1zwE3N4AAYxMIsEphZYow5pS7GJCE9duPGs89++Y/95p/Bpf/uO/7vm6/c/P3ff+PuvXuL+XIYBhBNJpOmyTk3KQYWEiICCCRMRNhjoiAcY+zaNoSAPQFAYAJg5uamqqZaShnGcXjPWMo4DGMZL6hqSun4yvGV4+PZbDaZTJq2bZsm5wxQqcVUzZyFY4xMbKb9brdarUw1X2hyk81svVoPw07NhDnnLCEEEQBVtZSy2+1SSg/cf/9kOkspiQgAVa1lBFHbNBKCMAOoqqam7uaGdzkDYCJiCSnGmGJKXdcezaaz2XQ2m+YmxyxB2HGBCO86ef4ODt781YcB/MCf/vqY88c3P4+Dv/2n/9GjH3jiwZNHZ/c/2HRTEIMJhHEcVsvl/Pzs7J23zs/vbZfz7WZZtlsrA6zCDebMlEJIKeaUQgjEDGJhUbV+N6gaMxu8qrFIblsOwdxjipPprGknsUkgGsYKWIhRRIhEHaYKAwGyF4IwAzCH1QI4C8G9jqOqwo2IYwzCTATdq6XWClcQBREQu0NVSy1V3R3mMMCqFtNSdSh12I3bvl+vNsvl8vx8fvfevO97QH4rv4BL3/sVf//xx9+fUz49O331lVde/vSLt2/f3varUnqQAwaoQwEzVzPzPYMbERNBSEJgYWJTHcexqoKcQEGEgwgxCwOEC473EDFFkdw0xKRFq9ZSq6nigEWELxARDhxw1aJqqmpmam6GA2JmYQYREwAiwgViYWEmwAE18z1zwE3N4AAYxMIsEphZYow5pS7GJCE9duPGs89++Zc889SHnvxQ0zRf9O1rXPpTxz+0mC+HYQDRZDJpmpxzk2JgISEigEDCRIQ9JgrCMcaubUMI2BMABCYAZm5uqmqqpZRhHIf3jKWMwzCW8YKqppSOrxxfOT6ezWaTyaRp27Zpcs4AlVpM1cxZOMbIxGba73ar1cpU84UmN9nM1qv1MOzUTJhzzhJCEAFQVUspu90upfTA/fdPprOUkogAUNVaRhC1TSMhCDOAqmpq6m5ueJczACYilpBijCmm1HXt0Ww6m01ns2lucswShB0XiHDy/B0cvPjIs9c+/joRAaDve/5rYkrddNrOjtvZ8Q/+i7+Mg1c+EdvpEThAGIxxGFbr1fz09PStt87P7y4XZ/16qbveyhhggAMQQAKnGHNKMUQJQsRurmpFqzmYg5n14wAgpMwi1S3mfHR03HZdyAnEYykAYojMAhEAVRWAQAgUghAxADOtVYkQAxHBzeEKUGCWmISJ4WpaxrGWUmp1UwDEFEJ0x1DGquqG6nCzov7tv/TVOPjx539l1+9W681yuZqfL+7dO9tuh38eX8DBn+TvefChB6+f3Hjf+z/YNO3Z2b1bt159+eUXX3/99X63KmVwqLuCFDBzdTOHubsZYACYWVhCkMAirjrshqrV4QCCCDMHCSwMEC74HhEBRExRJMZEjFLVVEuppgqAmIiYhQMzMePAzcy9VjVVg5sa4HgPsTCDiGkPIBywsDAT4IC7m7upAW5qOCAmIg5BmIRFQkgScoxNivmxxx597tkv/7Hf/DO49Le+9bdffeWVN+68eXZ2vlyuttseTF3T5q5tcxNjiEGYmYiEIEy4JEwphNw0MQYQwETMIGKQwffgDiK4V9Uyjn3fb/t+s9kMw1BLqaWaW87NlatXjmazbjJpm0bChRgCgKrq7sIXiJmIhamU2u96EE26rmu73LaljGdnZ+vVqowjmLq2y02OMTKLm9Va+n6XYrz/gfu7bhqDOFD3VLUWJk4ppxQlBABubntwd7zHiYhBYJEYU4wppNS17XQ2PZp10+m0aXLMIkKOC0R418nzd3Dw5q8+DOD7P/J1MaVPDJ/Awc/+G3/30Sc++MiN993/yLV2egQOEAZjHIbVejU/PT19663z87vLxVm/XuqutzIGGOAABJDAKcacUgxRghCxm6ta0WoO5mBm/TgACCmzSHWLOR8dHbddF3IC8VgKgBgis0AEQFUFIBAChSBEDMBMa1UixEBEcHO4AhSYJSZhYrialnGspZRa3RQAMYUQ3TGUsaq6oTrcrKhX1aK1VB2GcdfvVuvNcrmany/u3Tvbbgcz/FZ+AZf+8jP/w/ve/8Gmac/O7t269erLL7/4+uuv97tVKYND3RWkgJmrmznM3c0AA8DMwhKCBBZx1WE3VK0OBxBEmDlIYGGAcMH3iAggYooiMSZilKqmWko1VQDERMQsHJiJGQduZu61qqka3NQAx3uIhRlETHsA4YCFhZkAB9zd3E0NcFPDATERcQjCJCwSQpKQY2xSzI899uhzz375l3zZU1/85IdiSh/6ji0ufU3315fL1Xbbg6lr2ty1bW5iDDEIMxOREIQJl4QphZCbJsYAApiIGUQMMvge3EEE96paxrHv+23fbzabYRhqKbVUc8u5uXL1ytFs1k0mbdNIuBBDAFBV3V34AjETsTCVUvtdD6JJ13Vtl9u2lPHs7Gy9WpVxBFPXdrnJMUZmcbNaS9/vUoz3P3B/101jEAfqnqrWwsQp5ZSihADAzW0P7o73OBExCCwSY4oxhZS6tp3OpkezbjqdNk2OWUTIcYEIJ8/fwcGLjzx77eOvExEA+r6v/xMxpXYyzdNp6qY/8pkfwMFnfp4mx1cRAkRAGMu4Xq7mZ6fvvH1ncXp3uTgb1qsy9Kgjk5E7DIFJAueU2tzElGIIarbrd7UohIOEmFI12263VZUlOJOaSsqz2VGetBITSOAG4hgEIkxiABx7DCIw7TG7wUxLLQwPgUJgZglBolxgFiK4aVUtZSzDWMdRtZSqIpxzJuKxlKJuago3QzX9s7/41Tj4yY/8w+12t1lvFqvVfLGcny22m92v08/i4I/Tdz/04IPXrz/6xBOf17Xt+dn5rddefemlT332jdvb7Wosg2lxVMAcaq5u5lBTc4OBGcQsLDFIIGaYj+OoWs0MADOJCIsIMxEB5G4AiAggYgoiIUYAphdqraZmcAaxMDETEdMFAO5u7qbqe+YG3wPg5sS0xyBi2sPnIGYcuDngAHzPnJguMAcR3hMRCEkAYpSYcrpx47Fnnnvub774UVz6Gx/59Zuv3rxz585isd5u1v0wMtB07XtySiFKECISIiaYm9Zqpm7OhBACCwMwApjwOZg5hsBywc1KKeM49rud1gpAzaxqTHE6m7VtG0IgIq3VzEAEdwDMHFMSEYBSjl3bcRCrlUOYdpPUNCmGvu/feeud+XK+63ckPO0mk65ru1ZiZOJay3a7lRCuXrnaNFnNSxl3/a6U0dRIuMlNSilKCEHwLmaAmBl7JAyASFgkpRD3Utvk6WwynU5n00nbZoksQo4LRHjXyfN3cPDmrz4M4Pu/4WtjSp+ofxsHP/lVv/D4Bz7v5H3ve+jkscnxVYQAERDGMq6Xq/nZ6Ttv31mc3l0uzob1qgw96shk5A5DYJLAOaU2NzGlGIKa7fpdLQrhICGmVM22221VZQnOpKaS8mx2lCetxAQSuIE4BoEIkxgAxx6DCEx7zG4w01ILw0OgEJhZQpAoF5iFCG5aVUsZyzDWcVQtpaoI55yJeCylqJuaws1QTbVqqTqqjsO43e42681itZovlvOzxXazU/XfCD+HS3/p6b/zxBOf17Xt+dn5rddefemlT332jdvb7Wosg2lxVMAcaq5u5lBTc4OBGcQsLDFIIGaYj+OoWs0MADOJCIsIMxEB5G4AiAggYgoiIUYAphdqraZmcAaxMDETEdMFAO5u7qbqe+YG3wPg5sS0xyBi2sPnIGYcuDngAHzPnJguMAcR3hMRCEkAYpSYcrpx47FnnnvumWeeevKpJ0OMT/5HAy59Vfj+7WbdDyMDTde+J6cUogQhIiFigrlprWbq5kwIIbAwACOACZ+DmWMILBfcrJQyjmO/22mtANTMqsYUp7NZ27YhBCLSWs0MRHAHwMwxJREBKOXYtR0HsVo5hGk3SU2TYuj7/p233pkv57t+R8LTbjLpurZrJUYmrrVst1sJ4eqVq02T1byUcdfvShlNjYSb3KSUooQQBO9iBoiZsUfCAIiERVIKcS+1TZ7OJtPpdDadtG2WyCLkuECEk+fv4ODFR5699vHXiQgAfc/X/dEYQjudxMk0ttOfuP1f4OBf/jd1dt/9lDJEAIxlXK83i/Oz03fuLE7vrhbn2/WybLemA8MEYKLIklOIeyEyM9zHsazWG3PLqWnbJnedA9ttX6wy2IDiGmJsJ9OYGgpCLMJCIoGFgoAIYHcDQGAGMTMAM5hpqQpoZApJUk4pphyjsJirq9aqqqOqVlWvWquaKTFSSgCVWquZG9Th7lXto5/4ozj4qW/85HbTrzfrxXK1XKzm89Vm0/9T+xgO/pj/lQcefPD69Rsf/MAH27Y9O1+8dvuV33n55d9/8/Vtvy7jTq26VUAd6tA9h5qaGtxBxAwRDiQSRNy9lmqm7g44ETEzMQsxC+PA3YkIFyjGEGJkUDU11apqqm4OgJiImJgAEBEAdwfg5oDjwPfMDY4DBhETADcHYHAADCImHLg5LhETEYcgeywiTEQCCFyIJEq8duP6U089/fF/+edx6T/72v/1lVduvvX22+tNPwxjrSWI5KZp27brurwXg4gwEQAmN9VSq9ZaS4UrsTjMTNXdARD2hGUvBJEQYoxBhIgB36uqcAeRmWmtItJ2XYwRgNba930pRVXdQQQJoW2amCKH0LXt0fFxzhkOEclNjiGGGPq+v3v37mK+6PstgLZtu25ydHyUc0OEcRxXyxULX7lyNedkZmUc+74fxlFVAcQYc4gx5RiEWVhEgrAIswgJMREYBBHhEFhiCNI07dFsMptNJtNJ0yQJLEKOC0R418nzd3Dw5q8+DOB7/82vjiH8Av0yDn70uf/20Q88cfL4EyePPT67735KGSIAxjKu15vF+dnpO3cWp3dXi/Ptelm2W9OBYQIwUWTJKcS9EJkZ7uNYVuuNueXUtG2Tu86B7bYvVhlsQHENMbaTaUwNBSEWYSGRwEJBQASwuwEgMIOYGYAZzLRUBTQyhSQppxRTjlFYzNVVa1XVUVWrqletVc2UGCklgEqt1cwN6nD3qlZUx6K11n43bDf9erNeLFfLxWo+X202fR31N8LP4dJf/NJf/uAHPti27dn54rXbr/zOyy///puvb/t1GXdq1a0C6lCH7jnU1NTgDiJmiHAgkSDi7rVUM3V3wImImYlZiFkYB+5ORLhAMYYQI4OqqalWVVN1cwDERMTEBICIALg7ADcHHAe+Z25wHDCImAC4OQCDA2AQMeHAzXGJmIg4BNljEWEiEkDgQiRR4rUb15966umnvuyZL/uyp2NKz/wFw6U/5H9lGMZaSxDJTdO2bdd1eS8GEWEiAExuqqVWrbWWCldicZiZqrsDIOwJy14IIiHEGIMIEQO+V1XhDiIz01pFpO26GCMArbXv+1KKqrqDCBJC2zQxRQ6ha9uj4+OcMxwikpscQwwx9H1/9+7dxXzR91sAbdt23eTo+CjnhgjjOK6WKxa+cuVqzsnMyjj2fT+Mo6oCiDHmEGPKMQizsIgEYRFmERJiIjAIIsIhsMQQpGnao9lkNptMppOmSRJYhBwXiHDy/B0cvPjIs9c+/joRAaDv/po/FGLKky530zg5+unf/2Ec/Iuf3h4/+JA0LYXg7mUs2+12tZzPz+4uzk9X52fr1bxfLbXs2DUKpZialLomM4lpHXbDerPZbrb9bscs06PZ0Ww2nR2FEEctVc0Bd6/uEI65kRAAsMQYE4fAwkTiBIDMHYBAmPYYgDvMtFQFNDBiSl2Xc2qiCGDDOJZhGMddVSWCELOIEIEAYgLUrdaqDoAc7ICq/jsfew4HP/PN/2i93iyX6/l8sVislvP1etP/un8MB19V/9KDDz14cu36449/IOe8WMxfu/3a7/7u77z5xme3u02to0Hd1VHhalZVq7ntqQGKCyxCQuA9AG4OuMMBJxAxETMRCTMR4V8XQsgps7CpVVNTVTNTAxzvIfwBB4iY9nDge2amZnBc8j1zwN0cADET0x4+B4OIiUWCCIsEFmIQGBAgwATwBx568EMf/uL/+e3vw6Xv/iN//5WbN996+51hHM1MQkwhpKZpm9x2Xc45CjORucONAdvTqrWOpbiqA0XLOAyjFjUHIEFSTDGlFEMIkZn2ZC+EnFJMiZlVtZYyjCOAtm32ENROAAAgAElEQVSZ2czKOG42m2EYSlU3dYcEaZu27dpuOjk6OrrvvvuanFXV3JklioQYxlJXy8Vqvd5ut7VWIppOJvc/8EDXtOa23W5Pz84AXL1ytZt0gcXcdsNQxnEsRWs18yDS5BxTCiHEvZRjiiEEYWESgIlBLMxCIszUNM3RbDqbTfaaNrEQC8EBwh4R9k6ev4ODNz75EBH91a/9yhDTfx/+Lg7+y6f+1vXH33/t8fffeN8Hjh98SJqWQnD3Mpbtdrtazudndxfnp6vzs/Vq3q+WWnbsGoVSTE1KXZOZxLQOu2G92Ww32363Y5bp0exoNpvOjkKIo5aq5oC7V3cIx9xICABYYoyJQ2BhInECQOYOQCBMewzAHWZaqgIaGDGlrss5NVEEsGEcyzCM466qEkGIWUSIQAAxAepWa1UHQA52QFVL0V0p4zj2u916vVku1/P5YrFYLefr9aYfduNv5Rdw6bs+/IuPP/6BnPNiMX/t9mu/+7u/8+Ybn93uNrWOBnVXR4WrWVWt5ranBigusAgJgfcAuDngDgecQMREzEQkzESEf10IIafMwqZWTU1VzUwNcLyH8AccIGLaw4HvmZmawXHJ98wBd3MAxExMe/gcDCImFgkiLBJYiEFgQIAAE8AfeOjBD334i596+pk//Ie/IjfNV35vwqVnhr9oZhJiCiE1TdvktutyzlGYicwdbgzYnlatdSzFVR0oWsZhGLWoOQAJkmKKKaUYQojMtCd7IeSUYkrMrKq1lGEcAbRty8xmVsZxs9kMw1Cquqk7JEjbtG3XdtPJ0dHRfffd1+SsqubOLFEkxDCWulouVuv1druttRLRdDK5/4EHuqY1t+12e3p2BuDqlavd/88UvMbsml71Yf+vta7Dfd/P4T3tvWfvPZ6xxzMG2xhszFBk2tKkqFGNGEMaEawSgoJog0AhpUSN6Ie2SlNBcxCJIsCmUaOSOkKVAgRS5CogBYSEGrkpqFLIF5MYe2af3uNzug/Xtdbq8z7mdfj9Zl1gMbdhHMs0TaVorWYeRJqcY0ohhLiXckwxhCAsTAIwMYiFWUiEmZqmWS7mi8Vsr2kTC7EQHCDsvfyJJzj43MM3H/3cHwKgvR/7E98oUWI7y908z+Z/59nfwMG/+OvnJw8f5flCUlL1Uso0DpvN6ubyxerq4ubyfHVztVvf1LFnWBbucm6anFOG+7DrV+vV1cXlbtebI+Z8fHS0PFoul0cpNyA4oA6DK9yJSYSJDR5CjLkNIRLDiQzsDjcQIExEzCwEuMPM1BRwEeQY2q6JMTGRad31/Tj2ZRzMPYqkgxiSBAFQa6mqVdXdQQISB6rpJ/7Wh3Hw9z752c16s1qtr65urq9X11c3m3X/2/wzOPjY+JdO791/6aWXHj96OcfmZnX1pS998V///r968vydaepVC0jN1Vxdi3o1q6aq5q7mgDsTiEgAAohADBATEfaICSAALMwgFgaImNzc3dycRdquDRLcVc3NzFXNXc3cHPA93HFzFmYRplsA3F3NTFXNfM8ct9zM3F3NAAQRZgaImPZwR5iJOMbAIgQQMwyACCc3qqonpyevv/FV/2z8Cdz54Y/8w89//vPPzy9KKSBOKTU55Zybtp11XYqRmQHXWuFOBAbgbmZai2pVs6nWcejHMhnATDHGlFLTtDEGZnZ3VSNCjLFpmrbrmHn8smFw96ZpRARAqXUax6kUraqmrsZBUkpt07Sz2XK5OD4+jjGN02hVDR5CaJqGicdxGMZxHIZxnKrWtm0fPHjQtq2prtfrZ8+fw/3k9HS5WOaUSERrKaWWMo3TVKaJiNumySnFGFNKsWljDFEiizAJEUAEMAURFmJqcrNYzOaL2XzW5ZxECAJz7DGBCHuP33qCg7d/5QER/ZU/+TGJ8g/zP8bBf/fBv3n/8Ssvv/u1d732+snDR3m+kJRUvZQyjcNms7q5fLG6uri5PF/dXO3WN3XsGZaFu5ybJueU4T7s+tV6dXVxudv15og5Hx8dLY+Wy+VRyg0IDqjD4Ap3YhJhYoOHEGNuQ4jEcCIDu8MNBAgTETMLAe4wMzUFXAQ5hrZrYkxMZFp3fT+OfRkHc48i6SCGJEEA1FqqalV1d5CAxIFqWiYdyjSN02a326w3q9X66urm+np1fXWzWff9MP0/3d/Hne9/49OPH72cY3OzuvrSl774r3//Xz15/s409aoFpOZqrq5FvZpVU1VzV3PAnQlEJAABRCAGiIkIe8QEEAAWZhALA0RMbu5ubs4ibdcGCe6q5mbmquauZm4O+B7uuDkLswjTLQDurmamqma+Z45bbmburmYAgggzA0RMe7gjzEQcY2ARAogZBkCEkxtV1ZPTk9ff+KoPf+TD3/zN/3476z7+189w52s3PwjilFKTU865adtZ16UYmRlwrRXuRGAA7mamtahWNZtqHYd+LJMBzBRjTCk1TRtjYGZ3VzUixBibpmm7jpnHLxsGd2+aRkQAlFqncZxK0apq6mocJKXUNk07my2Xi+Pj4xjTOI1W1eAhhKZpmHgch2Ecx2EYx6lqbdv2wYMHbdua6nq9fvb8OdxPTk+Xi2VOiUS0llJqKdM4TWWaiLhtmpxSjDGlFJs2xhAlsgiTEAFEAFMQYSGmJjeLxWy+mM1nXc5JhCAwxx4TXv7EExx87uGbL336C0wHP/otH5UgITVxNs+zxU9f/hQOfv2/+YP7L7+yOD2LbatVS6lqpd+sL85fXF08uzp/vrq+3K2u6zhEQQ7SppxTCCJ1mtbr9dXV9fn5xTSVtpvN9xaL+WI+n83btos5URA3KOAMJ3KHAyDikHLbSggA3KEGB9wdICJmlj0mIoe7mxuTS5AQJUYJLGZayjT0/TgNtVQBYk5tk7t2llOSGNy9TFOpWrW4AyQgBlFV//hPfgAHP/M9v75Zr69vVjfXNxeX15fnN+vV5nfip3HwjbsfPD4+PT09u3fvAbOsbm6ePH3nD/7N5y8vX6hO6pVhBjWrZqVqcS3V1B1uMMUdAWAGZjCEhUSEmQDCATEJMQuzCAB3N9VSahBpZ12KEYCpGdzMXNXca1V3c3OD444ws0gQIdxyQM1Mtaqaqps7fM/M3U2rEpNICCLERMwAiMjdAQgxC7OIMBNuuYNImBuAtOp8sXz1lVd/J/807nzf+z71hS984eLyshYFkJucc5Ob3LVt23U5RgBuWmp1tygcRKIEEMHUTPdKrWMZqyoIHEKTm5xTzjmEYGal1nEYVJWIYow5N4Dv+n7o++1uB0fbtjmnvRAC9ogAmJnWCkBEwHsUU5rPZgB2fV9LAZByPlouc9MwM4BSyjRN4zimlO7du9fmptSyulk9ffbUzM7O7h0fH8/m85QS3My81DKO49D3ALq2y7mJIaQUY0oSkzABTCCAicEsJMIsLJJzms+7+WzWzdqckzAgMMceE4iw9/itJzj44j+5z0Q/9ie+SYJ8pvtlHPzV9/6Px/cfPnzXq6+8/r77L7+yOD2LbatVS6lqpd+sL85fXF08uzp/vrq+3K2u6zhEQQ7SppxTCCJ1mtbr9dXV9fn5xTSVtpvN9xaL+WI+n83btos5URA3KOAMJ3KHAyDikHLbSggA3KEGB9wdICJmlj0mIoe7mxuTS5AQJUYJLGZayjT0/TgNtVQBYk5tk7t2llOSGNy9TFOpWrW4AyQgBlFVn0oZhmks42bbb9br65vVzfXNxeX15fnNerXZ7frfO/p53Pnuh3/73r0HzLK6uXny9J0/+Defv7x8oTqpV4YZ1KyalarFtVRTd7jBFHcEgBmYwRAWEhFmAggHxCTELMwiANzdVEupQaSddSlGAKZmcDNzVXOvVd3NzQ2OO8LMIkGEcMsBNTPVqmqqbu7wPTN3N61KTCIhiBATMQMgIncHIMQszCLCTLjlDiJhbgDSqvPF8tVXXv2aD33oYx/75nYx/89/+nXc+cDVDwDITc65yU3u2rbtuhwjADcttbpbFA4iUQKIYGqme6XWsYxVFQQOoclNzinnHEIws1LrOAyqSkQxxpwbwHd9P/T9dreDo23bnNNeCAF7RADMTGsFICLgPYopzWczALu+r6UASDkfLZe5aZgZQCllmqZxHFNK9+7da3NTalndrJ4+e2pmZ2f3jo+PZ/N5SgluZl5qGcdx6HsAXdvl3MQQUooxJYlJmAAmEMDEYBYSYRYWyTnN5918Nutmbc5JGBCYY48JL3/iCQ4+9/DNBz/7b4mJiei/+pYPE4vEFNpZ7uY/t/kZHPziX/x/H777PfcfPs7drJRqbiDfbTcXz56eP396efFsdX05bFZap8SIwpmFAPdaxnG32a7Wm9XNGsDy6KibL9q2TU3T5KZt22beSYiubgwIG1DV3ECBQ8pt20mI5qbuZlAzNwBMTMISJIoQg3FLAYRILCxEBjfVMk1lmqoWmLJIzk2bU9O0KQaW6LBaaqlFVc0BYoAcqOYf/4kP4OBT3/sbm/XmerW6ub65vLh58eJyfbP6nfi/4OCjq/9icXS0WCyPlscA1qv1ixfP3n7nS+vVNaAGddfq1bVULaalmrqrmbsDBgPB4IA7YACDSQJziEGEiRmAmxOTELNICEJEVVVLncrELLNZl2LCgcHdzN1VtVY1VXVzcwDEtMcgFglBaA9wQM201qpqVc0NgJm7215VBRBFWIII7wGEOywszBIC0y13mBnAgRMgtepsPn/40qPfO/kHuPNn3/W33/7iO5fXV6VWd0iQlHLXtO1sNp/Pc44MmFktheAxhpxSTomZ4aaqtVY1NXcwWERCyHspxRDBpLWO09TvdtM0lVoBBJGqOuz6bb9brzdwy7mZH3Rdu5dzlhAAlFJUFYC5aa0AxZxMdbPZjNNExG2Tj4+P267LOYsIAFWdpimEcHR0lHMDt/V6/eTpUzM7PT09OjqazWYxJibsmVmpte97AG3T5pSihBAjByEWHLhhT1iIiUOUg6ZtF4vZYjbrZm3MUQQgmGOPCUTYe/zWExz84S/dJ8Zf+ZPfSCy/MP9VHPzI4/92fnx2//G73v36Vz1893vuP3ycu1kp1dxAvttuLp49PX/+9PLi2er6ctistE6JEYUzCwHutYzjbrNdrTermzWA5dFRN1+0bZuapslN27bNvJMQXd0YEDagqrmBAoeU27aTEM1N3c2gZm4AmJiEJUgUIQbjlgIIkVhYiAxuqmWayjRVLTBlkZybNqemaVMMLNFhtdRSi6qaA8QAOVDNp0nHaRqnYbvrN+vN9Wp1c31zeXHz4sXl+ma12Yz/38nP4863L//a0fIYwHq1fvHi2dvvfGm9ugbUoO5avbqWqsW0VFN3NXN3wGAgGBxwBwxgMElgDjGIMDEDcHNiEmIWCUGIqKpqqVOZmGU261JMODC4m7m7qtaqpqpubg6AmPYYxCIhCO0BDqiZ1lpVraq5ATBzd9urqgCiCEsQ4T2AcIeFhVlCYLrlDjMDOHACpFadzecPX3r01R94/7/3Td/UzWY/9I++EXfeePHn3SFBUspd07az2Xw+zzkyYGa1FILHGHJKOSVmhpuq1lrV1NzBYBEJIe+lFEMEk9Y6TlO/203TVGoFEESq6rDrt/1uvd7ALedmftB17V7OWUIAUEpRVQDmprUCFHMy1c1mM04TEbdNPj4+brsu5ywiAFR1mqYQwtHRUc4N3Nbr9ZOnT83s9PT06OhoNpvFmJiwZ2al1r7vAbRNm1OKEkKMHIRYcOCGPWEhJg5RDpq2XSxmi9msm7UxRxGAYI49Jrz8iSc4+NzDNx/87L8FiBj0I9/ytSRMHCU2cbb438a/j4N/8Ml//sprrz985dVusVQ1ACIy9JunT95+8fzp1cWLzeqq9FtYScLspnUqwzgMfRknVzUzgGNKs/ks5YZZmBlEMaX5YiExmpmDWMSAUauDooTYtl3XSYxqVtXNVBVuBjCxBJYQRSQwMTEBzgRmAOpuam6qaupqxAjCMYSYUgxRmAjsMNxidS1V1bDngDuK6Sf+xodw8Knv/Y3NZrtar1c3q4vLm4sXl6vrm9/mT+Pgo6sf6Lp5O2vbplWz9Wp9dX11fv5iGLZRRKHjtNM6VS2mtdpkqr5nZthjAG7kDhgcDoBAQSTEEGMgZiJydwBCzCIxBgKq2TRNZZqIqGnanBOIcMdVq5nWqmamBjhuETEJMTERcQiCAzXTUquqmbq7mTsc7mZWVd2chYSFRYSZ9kAAiImYg4iEIMzYc1QzNwAMCEFm89nZ2b3fv/8Z3Pn2s7/24tmL6+vVMI21aDVNMc1n3XyxXC4XTdvGIHCvVYXRpNTknNtGmK1qqWWcRhDlJsecUrwlIQQRAGZWShmnaRyGvu93fT/2Q6l1nKYyTbvd9vrmRos2bTOfz09PT49Pjk9PTpbLZdt1wlz2ajW1qnUsk5uBqIzTerMupTBzznk+n7dtG1OKIcYUmRlAEGnaNscUUxrH4fmLF6a6PDrq2lZCJII7hElCBKC1EDjGEEIUFmY4kburqqmpORzEEoKEkCRJCGHWzY5Pjo+Wi7ZrUw7MAMEce0wgwt7jt57g4A9/6R6AH/vWN0n4/5j9Gg7+y5MfzbPl6cPH73njq1957fWHr7zaLZaqBkBEhn7z9MnbL54/vbp4sVldlX4LK0mY3bROZRiHoS/j5KpmBnBMaTafpdwwCzODKKY0XywkRjNzEIsYMGp1UJQQ27brOolRzaq6marCzQAmlsASoogEJiYmwJnADEDdTc1NVU1djRhBOIYQU4ohChOBHYZbrK6lqhr2HHBHMa2TTmWaxmk3DpvNdrVer25WF5c3Fy8uV9c3m03/u8ufx53/JP9427Rqtl6tr66vzs9fDMM2iih0nHZap6rFtFabTNX3zAx7DMCN3AGDwwEQKIiEGGIMxExE7g5AiFkkxkBANZumqUwTETVNm3MCEe64ajXTWtXM1ADHLSImISYmIg5BcKBmWmpVNVN3N3OHw93Mqqqbs5CwsIgw0x4IADERcxCREIQZe45q5gaAASHIbD47O7v3+htvfPSjX9923Y//n38Kd979zidr0WqaYprPuvliuVwumraNQeBeqwqjSanJObeNMFvVUss4jSDKTY45pXhLQggiAMyslDJO0zgMfd/v+n7sh1LrOE1lmna77fXNjRZt2mY+n5+enh6fHJ+enCyXy7brhLns1WpqVetYJjcDURmn9WZdSmHmnPN8Pm/bNqYUQ4wpMjOAINK0bY4ppjSOw/MXL0x1eXTUta2ESAR3CJOECEBrIXCMIYQoLMxwIndXVVNTcziIJQQJIUmSEMKsmx2fHB8tF23XphyYAYI59pjw8iee4OBzD9988LNfABwA/ci3fq0CboyQcrv8Bft5HPzdb/snr7z2xnve+/rRyZmBmDkIb7frd9750vnzp9dXF/1mpVNP0CTsWqexH3e7fre1qoEl55Sbtm2apm1JpKqaugEcQtM2wlJVHSARB6ZSjRBTapuuWyxCSmZeqmotVR0OkDCLsASRECWEwCJEAJwIWqdh6KtqEGEiYiZCYJEgTUosYmbubmZMHEIAYapq5g6ow11L1U/8zQ/h4Ge+5zd2u91ms1mt1tdX1+cvLi4vb36bPoWDj6z+Qpvb1OSUkpnvdtvVzfry+nwchhBErQ7jVutUTV1r1Wqm7uYGg+MWucEdMDicsEdBRAITiQgTEwFgDiQsEqM4UIsWLdM0MVGTmxgD7YGJCXAzq2ZWa1U1MzcnJgKxMIMAIgYRAwS4qVVVM1U1hwMOB+BmpuYOg4OIAxNJECYiJoCYiDmIsAgRM8HcTd3V1d3BQtLOZqfHJ59/1y/izre2f/Xi8nqz3ozjNNViqhLirOsWy+Xx8fFsNksxELG7BeYUY0oppihCbl5rLaWwcDeb5ZxjiiLi5oCXektVTbWqTuPY9/1u1++N06S1bHe79WpdShGRrmuXy6PTs9OXHrx0enrSdR2L1FJV1UzVDHBTq6rDOKzW62kqTBCWEGOOMTZ5r22aGJMECRJyyjGnnFKZpsvrK6111s0liKrWWtWUiWJMIgIHC8WQYoggYmIwYFBXczBALCGIhMBBYogxpcV8fnZ2ulgsmibHHJhBBHeAQIQve/zWExz84S/dJ8Z//ae+QYFfbD+Lg++d/1Bu5sf3XnrlvW+88tob73nv60cnZwZi5iC83a7feedL58+fXl9d9JuVTj1Bk7BrncZ+3O363daqBpacU27atmmatiWRqmrqBnAITdsIS1V1gEQcmEo1QkypbbpusQgpmXmpqrVUdThAwizCEkRClBACixABcCJonYahr6pBhImImQiBRYI0KbGImbm7mTFxCAGEqaqZO6AOdy1Vx6lMU52mqR+G3W632WxWq/X11fX5i4vLy5vNeve7y5/Hnf+IfjSlZOa73XZ1s768Ph+HIQRRq8O41TpVU9datZqpu7nB4LhFbnAHDA4n7FEQkcBEIsLERACYAwmLxCgO1KJFyzRNTNTkJsZAe2BiAtzMqpnVWlXNzM2JiUAszCCAiEHEAAFualXVTFXN4YDDAbiZqbnD4CDiwEQShImICSAmYg4iLELETDB3U3d1dXewkLSz2enxybteffX9H3h/27Z/53PfhzuvfPG7plpMVUKcdd1iuTw+Pp7NZikGIna3wJxiTCnFFEXIzWutpRQW7maznHNMUUTcHPBSb6mqqVbVaRz7vt/t+r1xmrSW7W63Xq1LKSLSde1yeXR6dvrSg5dOT0+6rmORWqqqmqmaAW5qVXUYh9V6PU2FCcISYswxxibvtU0TY5IgQUJOOeaUUyrTdHl9pbXOurkEUdVaq5oyUYxJROBgoRhSDBFETAwGDOpqDgaIJQSREDhIDDGmtJjPz85OF4tF0+SYAzOI4A4QiPD4rSc4+NzDNx/87B8CTgz6y9/29aXUsVRQyN3xL9Ev4OB/+g//91fe897X3/eBew9eyrlhFjO9WV2/887bF+fPNqvrcdjCJnJNwma19H2ZhjKOAmrapm27xWyWmiYEqeb9OJgZSwCRm1fVqRQz5yDmPtUKopBy181Pjk9TStWtlDqNUzUwk4iwRCYCEGJMOccYmQDC3rDbXFyea/Xl0bxpWiK4oZSJmdpuFkSqqhvgJiK5aYhETc3gMDMvplMpb/3P78fBz3zy13dDv9vuVuvN9fX1i+fnVxeXv0WfxsHXXn1P27SpyTk3DIzjtNmuzi8u++22WlUttZSq1WGuambq5mbm5uYGg8Fxyx0wkHBgIcD24AAJs0SJEkMMLEJE7m5aq6pVJeYYI4swETMxGExurq5aa6lqVR1OIA4cJBCRqgHuANzd3MxU1d3MHDDcYmIiAkDuXqsCLkzMQiJCzExgImIBkTABDsDdzdVdq7o7iJqmPT49ffLe/wt3PuY/fH293vW9FnUYMUuIKebFYn52djafz1OKIQQ+EBbcchawCBG5ewyhbdoYIzGp6vhlw6CqzCwhxBgBlL1pGvemg3Hq+36apqpKQIjh6Ojo0aNHJ0fHIUVhNr9lqjHG+XzOzOM07nb9ar0eh8FUSynjOIHRtV17kHOO6VaTUkxRJJRSV+sbLTWmWNV22+0wDLUUM2MRZhaRGFPbNiEkAXMMMUURYRIJ0uQUc9oLQRwkITZNWi6OTk9PZrNZzCEEESEi3CJ8mTte/sQTHLz9Kw8B/Oi3v1lK/aX8WRx8sv2B3CyWp/cfPn71lfe89/X3feDeg5dybpjFTG9W1++88/bF+bPN6noctrCJXJOwWS19X6ahjKOAmrZp224xm6WmCUGqeT8OZsYSQOTmVXUqxcw5iLlPtYIopNx185Pj05RSdSulTuNUDcwkIiyRiQCEGFPOMUYmgLA37DYXl+dafXk0b5qWCG4oZWKmtpsFkarqBriJSG4aIlFTMzjMzIvpVMrQj+M47Q19vxv63Xa3Wm+ur69fPD+/urhcrTe/u/wM7vwH/sM5NwyM47TZrs4vLvvttlpVLbWUqtVhrmpm6uZm5ubmBoPBccsdMJBwYCHA9uAACbNEiRJDDCxCRO5uWquqVSXmGCOLMBEzMRhMbq6uWmupalUdTiAOHCQQkaoB7gDc3dzMVNXdzBww3GJiIgJA7l6rAi5MzEIiQsxMYCJiAZEwAQ7A3c3VXau6O4iapj0+PX1w7/7jl1/OTfvLL/4H3HnXF/4zhxGzhJhiXizmZ2dn8/k8pRhC4ANhwS1nAYsQkbvHENqmjTESk6qOXzYMqsrMEkKMEUDZm6ZxbzoYp77vp2mqqgSEGI6Ojh49enRydBxSFGbzW6YaY5zP58w8TuNu16/W63EYTLWUMo4TGF3btQc555huNSnFFEVCKXW1vtFSY4pVbbfdDsNQSzEzFmFmEYkxtW0TQhIwxxBTFBEmkSBNTjGnvRDEQRJi06Tl4uj09GQ2m8UcQhARIsItwt7jt57g4HMP33zp019iwh79yFtfX0rdDhMgbbf8Ff7HOPjxr//ZV979+vs/8KFHj9/VzRcsoUzTen399NmTq/MXq/V136+tDAyNzIB5nUyrqwaRNjddeyvEqIBZnWpl5pRaB4ahn8ZxnKZSDUxmNk4FQEh5vlicnJzlpjW3WuowTmqQICwiLAA5EGLMTU4xMgtgMN9uNs/Pn8H97Oys62ZgqOrQD0TcdW0IUtXcQMQsknMiFjdzdwBqKFqmafq2n3wfDv7en/31vt/ttrvNbnt9fX3+4vzy6vK37NM4+NDVJ1NKOaWYEhHVWrebzeXV1Wa7rWWqpVRVc4W5w8zc3NzM1B1m5g7DLQbgbkwSYwAwleJqDmeRFP8IEwNweFV1NYcTcwyBRQAQkRDjQN2salU1VYcTiEWCCICqam4wd7iZqZq7makZvkKYiYmIAVetZmAGs7CIEO+RMIGYCXfMHIC5aa2q6vDcNEfHJxdf/Zu485Hh+zeb3TRMpkbMIYiEFFNazOb37t+fz2cxxWtZi5MAACAASURBVBCCkEAIe+5qlZljijEEJhaRnBITVdVhGLabze5Wr6YpppxT13UxJSJyd92rdSqlljJNZSrTnlYF0HbtSw8ezGYzEOGAiBjUdt3Z2WmMaRyH7Xa33W3HYZhK2e12N9c3VbVr29zktm3zXso5p5hSEDH3aZo2m43WGkNU0+12N06j1mpmAJhZQsgpz2azHBsKkmJq2ibEKCIxhjY3uU0xJWYxtyCxm7XL5dHJ6Uk360QkBBYBEYHwZe4wxyvf8QQH7/zqIwB/+RPfUEr95fBZHHxX/guSuvny9P6Dx6+8+/X3f+BDjx6/q5svWEKZpvX6+umzJ1fnL1br675fWxkYGpkB8zqZVlcNIm1uuvZWiFEBszrVyswptQ4MQz+N4zhNpRqYzGycCoCQ8nyxODk5y01rbrXUYZzUIEFYRFgAciDEmJucYmQWwGC+3Wyenz+D+9nZWdfNwFDVoR+IuOvaEKSquYGIWSTnRCxu5u4A1FC0TNO03fXjMPbD3tj3u912t9ltr6+vz1+cX15drq7Xv7v8R7jzsfIDMSUiqrVuN5vLq6vNdlvLVEupquYKc4eZubm5mak7zMwdhlsMwN2YJMYAYCrF1RzOIin+ESYG4PCq6moOJ+YYAosAICIhxoG6WdWqaqoOJxCLBBEAVdXcYO5wM1M1dzNTM3yFMBMTEQOuWs3ADGZhESHeI2ECMRPumDkAc9NaVdXhuWmOjk9Ojk/OTk9jbn/bfhp33vWF7yTmEERCiiktZvN79+/P57OYYghBSCCEPXe1yswxxRgCE4tITomJquowDNvNZnerV9MUU86p67qYEhG5u+7VOpVSS5mmMpVpT6sCaLv2pQcPZrMZiHBARAxqu+7s7DTGNI7Ddrvb7rbjMEyl7Ha7m+ubqtq1bW5y27Z5L+WcU0wpiJj7NE2bzUZrjSGq6Xa7G6dRazUzAMwsIeSUZ7NZjg0FSTE1bRNiFJEYQ5ub3KaYErOYW5DYzdrl8ujk9KSbdSISAouAiED4ssdvPcHB5x6++ejn3ibCHv3QWx/WYv04gSQ3i3/Kv4iDH/mqv/Xaa2988IMfefnd754fnUgIOo6b3ebm+vLi8sXF+bPVzeW43ahOKXEUTkxMFJkCcwyRRQJLhU2lgCk3Tdd2bTcHsN1t+6GfhnEqtZqVWvp+NHjOuZsvjo9Om24GglcdSnFzlsAiRAyQw0MIqWliTAyYq1ft+361uiGi0+OTdtaSiKoOwwAg5SYGcQdAxHvCBBC5Y4+IzVG0jOP48Z94HQd/9898dtf3/Xa32W6uV6vLi8urq6vftE/h4GuuvitKlBCCCIBSp+22v7657ne7qrUWNa1m5nBzd3PATN1hZnC4u+EOERMoxuDutaqpAiDhGEIMUWIQZgBm7vA9mJNwSimwOAx/jDl8T62qOlyYmZiDmFkt1VQd7ubq5mampmYOxx0CMYPAANQMgDCzMDEHEWImImEmIvwx7q5mqrWaumtKabFYXn/wd3DnQ5s/12/7qVQ4EXGMMcSYUrNcHt27dzabzyQEZsaBuQEORwiS2ybGKMJMzI46lX4cNpvNerXebjfDMJi5iLRtO5/Pu1nXtm1OiUXgXlW1VjMrtY7jaKpElFJquy6GoPZHQghNyvPF4uzsNOdcSh3HYRzHYRxrKTer1fmL837oYwgppXwQ4i0RAVBK6Xf9arUyt1nXhRCqKtzxFUTMnHMzny3atosh5qbpZm2MCQRmiUEkSmCBkLvHkGaL+fHR8vjkuOtaEWFhESLGV7jDHK98xxMcvPOrjwD88Ce+Xov9SvgsDv50+HPg1M1Ozu4/eu21Nz74wY+8/O53z49OJAQdx81uc3N9eXH54uL82ermctxuVKeUOAonJiaKTIE5hsgigaXCplLAlJuma7u2mwPY7rb90E/DOJVazUotfT8aPOfczRfHR6dNNwPBqw6luDlLYBEiBsjhIYTUNDEmBszVq/Z9v1rdENHp8Uk7a0lEVYdhAJByE4O4AyDiPWECiNyxR8TmKFrGcdxs+2HX90Pf97td3/fb3Wa7uV6tLi8ur66u1uv1v5x9Bne+YffngwiAUqfttr++ue53u6q1FjWtZuZwc3dzwEzdYWZwuLvhDhETKMbg7rWqqQIg4RhCDFFiEGYAZu7wPZiTcEopsDgMf4w5fE+tqjpcmJmYg5hZLdVUHe7m6uZmpqZmDscdAjGDwADUDIAwszAxBxFiJiJhJiL8Me6uZqq1mrprSmmxWB4tT/Zyzv93+F9x55UvficRxxhDjCk1y+XRvXtns/lMQmBmHJgb4HCEILltYowizMTsqFPpx2Gz2axX6+12MwyDmYtI27bz+bybdW3b5pRYBO5VVWs1s1LrOI6mSkQppbbrYghqfySE0KQ8XyzOzk5zzqXUcRzGcRzGsZZys1qdvzjvhz6GkFLKByHeEhEApZR+169WK3ObdV0IoarCHV9BxMw5N/PZom27GGJumm7WxphAYJYYRKIEFgi5ewxptpgfHy2PT467rhURFhYhYnzF47ee4OBzD9989HNvE2GP/uJ/+qGqOpbikBRnn82/ioPvf/jfv/aeN77uIx999bXXj87ux5TKOOw26+ubq8vLFxfnz1c3l/1u4zalKClKDhKZAzMRCAR3U62qYy0xxflyuVwsunYG0LbfDeNQitaqDhunabPblaoSQtvNTo5P23ZGIu5WalWDMBEJiHEgIaSmCSLmMC1WapmmYehZZLlctE3LQcx8KhMcEkVYsEfCTARWN7gDxLfEHZOWcRw//hOv4+CnvvPXdn2/3W43283NanV9dXV9ffOb9ikcfM3lnwHxLSI92G536/XNOEzmpmZaqroBcHPAb5k7zAx7DscdAjGDWQDUquYKAwkHFokhiBAzvsIcAIuknIRY3fwAf5y5uQGgPRAJu3spRUsFoG5uDripqZnD3Q17BjD2iJhAAJhBYBYWEWYmZiZmplsg/DtublVrqdW8BEndbLH98L/Ana+6/O5hGEwNYCaJMaaUU86L+fLe/fuzWQdhAKpqtxRwIkoptm0bc+I9IlJM07jeW61vVjf9ru+H3lSJJOU0P1guF13bhhiZ2VTtoNQ6jSOAlFIIQUJgIjNT1VprCKGbzZaL5dnZaUpZtapqVdVaS62rm9WzZ083mw2LxBBijCFGYWEmM1PVcZqGvl9vNky8WC7apgHAIkFEwq24F2LOuW26mJoYQ0pN27UhChyAgVkYRASwwmKMi8Xi6Pjo7PSkbbsQhIREiAhf4Q5zvPIdT3Dwzq8+AvCDH/+6qvpr+ddx8G31uwzStEenZw9fe88bX/eRj7762utHZ/djSmUcdpv19c3V5eWLi/Pnq5vLfrdxm1KUFCUHicyBmQgEgrupVtWxlpjifLlcLhZdOwNo2++GcShFa1WHjdO02e1KVQmh7WYnx6dtOyMRdyu1qkGYiATEOJAQUtMEEXOYFiu1TNMw9CyyXC7apuUgZj6VCQ6JIizYI2EmAqsb3AHiW+KOScs4jrtNv9ttt33f73a7vt9ut5vt5ma1ur66ur6+WW/W/7L7DO587c13M5EebLe79fpmHCZzUzMtVd0AuDngt8wdZoY9h+MOgZjBLABqVXOFgYQDi8QQRIgZX2EOgEVSTkKsbn6AP87c3ADQHoiE3b2UoqUCUDc3B9zU1Mzh7oY9Axh7REwgAMwgMAuLCDMTMxMz0y0Q/h03t6q11GpegqRutlgul8dHy5Tb35t9Bnfe9cXvYJIYY0o55byYL+/dvz+bdRAGoKp2SwEnopRi27YxJ94jIsU0jeu91fpmddPv+n7oTZVIUk7zg+Vy0bVtiJGZTdUOSq3TOAJIKYUQJAQmMjNVrbWGELrZbLlYnp2dppRVq6pWVa211Lq6WT179nSz2bBIDCHGGGIUFmYyM1Udp2no+/Vmw8SL5aJtGgAsEkQk3Ip7Ieac26aLqYkxpNS0XRuiwAEYmIVBRAArLMa4WCyOjo/OTk/atgtBSEiEiPAVj996goPPPXzz0c+9TYQ9+r7/+A2tXmoxSAjtP1/+Mxz86fyXXnvtfR/96JvvfeOr7z98lJt2HPrVzer586eXVy/W65uxX5cyEKxJEoUDgxxwg1rVqlOZalEzFs5duzw6ms/mqWndre97VZMQAgtExjKtV+u+H6pbzs1icdx2s5gSCFrV3EAMEMDYYwQJMTXEpLVqKbVWr0XdU5CmbVPOEoSJ1RR7BIAABkBEDjc1AxjMcgtAUR3H6eM/+ToOfuo7/ulut9tst5vt5nq1urq8Wa1vfss+jYOvfvGd7tjzPbNpKuPQr7fbUgqB7P+nC15/vU2v+rB/11rX4b7v32Gfn5nxgMdzto0pYA9R3LhxIDYYEhv8olKlVk2rRgFB27+hal5Uaivxqi8CbSpFPalpaWqrtCUNEZT0ECwoNYFQqMtp7Afb8+z9O92n61pr9d57vJFNxOfjZlXVzc1xzxcA3Bx/BmJy82rqbjCAEUSYhUWYiEDEBIBAYAoiIUYmVDVTVTMABALATADMHA+YaeHupRRbuLs54AszNYO5YmFwOB4RE5MIMzERsQgLCwkTSIQJhAUT7jkANyu6KKXMLNI0q/mtX8Ojl5/+6DzN6hASkbgIMabcbDdn19dXbdc5oKbjNGldKGAhSEqp7doYIwtHliAyj/Pt3e1+vz8ej+MwzPNcSi21CHPO7Xrdbbfbpm1TSkTk5row1arjNMYQ15t12zTED4iq6jxNzLxarbdn2/Pz85wbUwUcIGYy8+Px+Md//PR4PDKziIQQQASgltL3wzTPbjqXMo1TiOHi4qJrWxAFkRhj27bre5vt2VkOyRwLFgkhxBSJxEzVlAggMMEc5hZjXK3W55fnT57cbNYrIiYmFhDhXe4wh5u/97NP8eDtz71AhH/tL7+h1f/B9pfw4C/t/2pVpLw+u7h5+eXXP/zht1557c2b51/ITTuNw363/+pXnz67/drhsJuGQykjwZokUTgwyAE3qFWtOpe5FjVj4dy127Oz9WqdmtbdhmFQNQkhsEBkKvNhfxiGsbrl3Gw25223iimBoFXNDcQAAYwFI0iIqSEmrVVLqbV6LeqegjRtm3KWIEysplgQAAIYABE53NQMYDDLPQBFdZrm/nTqT/2pH4bh1Pf98XQ6no53+/3ts93+sDsejr+6+s/x6I2v/qgvzOa5TONwOJ1KKQQyN6uqbm6Oe74A4Ob4MxCTm1dTd4MBjCDCLCzCRAQiJgAEAlMQCTEyoaqZqpoBIBAAZgJg5njATAt3L6XYwt3NAV+YqRnMFQuDw/GImJhEmImJiEVYWEiYQCJMICyYcM8BuFnRRSllZpGmWa3Xq7PtNqTmd65/Fo9e/P1Pi8RFiDHlZrs5u76+arvOATUdp0nrQgELQVJKbdfGGFk4sgSReZxv7273+/3xeByHYZ7nUmqpRZhzbtfrbrvdNm2bUiIiN9eFqVYdpzGGuN6s26YhfkBUVedpYubVar09256fn+fcmCrgADGTmR+Pxz/+46fH45GZRSSEACIAtZS+H6Z5dtO5lGmcQgwXFxdd24IoiMQY27Zd39tsz85ySOZYsEgIIaZIJGaqpkQAgQnmMLcY42q1Pr88f/LkZrNeETExsYAI73LHi5/5Ch584fm3XvjptwEQgf7Vv/SqqZWi5swh/9LFL+DBD9W//tLLr334w2+98eYHn3/x27quG8Zxd/fsy195+/bZ14fhWObBrACWhIQcbjCDGtwWqnUBQFJMbbNerVLThBDcMc0zQE3TNrmJOVXV/eHY9/00T8yhW2+6tou5EaaqauYADAsBg4lYJIbk7uM8l2mu8wj3ECTGkFMKcZEkCDMBbG4ON8PCHHBTdYeBWFgkCEBmPk/zD/8Hb+DBT33m833fH4/H/fF4t9/dPtvt9rt/xP8xHrz29NNWVd3c4K6l6jROw9BXVSZ2uJaqbm6Oew7A3fFnc3OHqbmrYcEgYmEiMDERMTExETMTMxOHGACYalV1MzcnJgKBCQtz/AkmmKupqgEOwBfmDjODw90NBocDcDcATBKiMAsRARREmJmECSTCBMKCCQtzwA1eVYuWWiYC5dzqn/8NPHrx93+4qrqBJQYOEiXGnFNeb8+ur6+6rlWHah3GUWtVUyaIhNw0q1UTYgQQiKPINM27u93pdJymucxzVZ2meZwGU2WRnPOqW6WUWARupVRzM703zzXldHV5uVp1IUQRDiGo2TRNRLTqum612m63MUY3AyimGEMIMQ1D/9WvfnXoexHhB2ZWShnH8bA/jNNUVcs8D+OQYrq6ulyt1swUY8w5t113dnZ2fnZ2fn4RU6pF3UFEQWJMAcRmqqbEYMK71BFj2Gy25xdnT57crNYrImIGEUBYuMMcCzd/72ef4sHbn3uBCH/t+143tX9w9st48LFnP1iKxbjabK9fevm1D3/4rTfe/ODzL35b13XDOO7unn35K2/fPvv6MBzLPJgVwJKQkMMNZlCD20K1LgBIiqlt1qtVapoQgjumeQaoadomNzGnqro/HPu+n+aJOXTrTdd2MTfCVFXNHIBhIWAwEYvEkNx9nOcyzXUe4R6CxBhySiEukgRhJoDNzeFmWJgDbqruMBALiwQByMznaT71p6Ef+qEf+qHv++PxuD8e7/a722e73X53PB5/fftf4dFLf/gpN7hrqTqN0zD0VZWJHa6lqpub454DcHf82dzcYWrualgwiFiYCExMRExMTMTMxMzEIQYAplpV3czNiYlAYMLCHH+CCeZqqmqAA/CFucPM4HB3g8HhANwNAJOEKMxCRAAFEWYmYQKJMIGwYMLCHHCDV9WipZaJQDm3bdutN+uUmj/4tp/Do+f/v78aOEiUGHNOeb09u76+6rpWHap1GEetVU2ZIBJy06xWTYgRQCCOItM07+52p9NxmuYyz1V1muZxGkyVRXLOq26VUmIRuJVSzc303jzXlNPV5eVq1YUQRTiEoGbTNBHRquu61Wq73cYY3QygmGIMIcQ0DP1Xv/rVoe9FhB+YWSllHMfD/jBOU1Ut8zyMQ4rp6upytVozU4wx59x23dnZ2fnZ2fn5RUypFnUHEQWJMQUQm6maEoMJ71JHjGGz2Z5fnD15crNar4iIGUQAYeEOc3z7j3wFD77w/FvP/a23mUAE+jc+8WZVnadqRuD0S5e/gAc/MP2197706nd990fe/+Z3fPtLL3Xr9TSNz27fefvtP7p99rVp6ksZzYpphVX3aqXAjAERySGwMBOTsIhQFOJ7BvKFGUtou67tuq7tmHkYx36cTqfe3VNum7ZtmoZZqqqpOdwAhoBBLMQkHKuWoe+HYSjjQIS2aWNKzBRDTIucck7MYg4zVVMzFFWYuboDBrCQiIDYDaXMn/mpD+HBT33mc8djfzwd94fD7u7ua+882+12/7j5O3jw0h9+ylTN3MwcMNVS5nGcqmoQUbVaSlXFNzgAd8e3cnM8cpiauxsMDicQFgwiJhAzmIWIgwgzkzCBAKhWW7gDYCICEZObOxyAuQNgInN3c3cD4OYAHAbADAuHuxsWBocDIKYokYUBIiYhXoCJiZlpAYBAABwOuLot5lLmeQSQUsMf+6d4dPO7n6yqAIIk5hBkEVJu1pvNkyfPrbrWCUV1nmYzBUFEUkptm7umlShqBneY12k+nU5zqczExOY2z/Nhf5imUVUBSIgASplrKfNcFgBMzeFN01xdXW0269y0OaecEoBaFUBKMS+aRkRUVZjzvWa9Xqnqs9vbMs8SAjPDvNQyDEM/DP2pP/Wn/tQfT8fj4SgiV9dX28025ZRzbnJumna1Xq1X60VuWgIxCzPHmHJOHASOBQmEiZiJyIlzTtvt2cX59uLyvGlbZhABhIU7FubuhsV7P/sUD97+3AtE+Ouf/EBV/fnVL+LBx77+yaKI0nWb8/e+9Op3ffdH3v/md3z7Sy916/U0jc9u33n77T+6ffa1aepLGc2KaYVV92qlwIwBEckhsDATk7CIUBTiewbyhRlLaLuu7bqu7Zh5GMd+nE6n3t1Tbpu2bZqGWaqqqTncAIaAQSzEJByrlqHvh2Eo40CEtmljSswUQ0yLnHJOzGIOM1VTMxRVmLm6AwawkIiA2A2lzMMwDuMwDtMw9Mdjfzwd94fD7u7ua+882+12h+Pxt65/Fo9e/NInzMwBUy1lHsepqgYRVaulVFV8gwNwd3wrN8cjh6m5u8HgcAJhwSBiAjGDWYg4iDAzCRMIgGq1hTsAJiIQMbm5wwGYOwAmMnc3dzcAbg7AYQDMsHC4u2FhcDgAYooSWRggYhLiBZiYmJkWAAgEwOGAq9tiLmWeRwApNU3TrrpVSumPX/sFPLr53U8xhyCLkHKz3myePHlu1bVOKKrzNJspCCKSUmrb3DWtRFEzuMO8TvPpdJpLZSYmNrd5ng/7wzSNqgpAQgRQylxLmeeyAGBqDm+a5urqarNZ56bNOeWUANSqAFKKedE0IqKqwpzvNev1SlWf3d6WeZYQmBnmpZZhGPph6E/9qT/1p/54Oh4PRxG5ur7abrYpp5xzk3PTtKv1ar1aL3LTEohZmDnGlHPiIHAsSCBMxExETpxz2m7PLs63F5fnTdsygwggLNyxMPdv/5GnePCF59967m+9zQQi0I/94AdLrcM41wpQ+OXrX8SDT/b/8ntefOk7vvN73nzjA+975bVus56mYXd3++Wnb9/dPRuH0zwPOk9qs2kxra6VgBxCTqnNTUopBCFhCLt7qdXcDMR0jyWEEFOTV91KYnBHKbU/DdUshBhTatqWWaqqqaobwCBiMAkRM0Cl1P1hP5yOQ38KTNvNNjWZiEQkx5SbpuvaEJK5qpmpqnk1s+rm6mbmboAwEbE5StV/8T/6MB78h3/lv+v7fn847Pf7u9u7r7/z7G6//9X1f4EHL37pE2rm7liYOVDKPI1zVQ0i5jZNk6rigbsDcHM8chgAM7zL4e4Gw8LheOBuAIiYQGAEWQRiDiIEAhPM1dTc8UCIiQmAm6ubm7k7ACIC4AtzAA7DP8MMC4e7GwwkHEMgYmJiIgIREzHTAgSAmfANbuYGN9WqZZomACGm+PHfwaPz3/y4mQMkHElEmELMbdtuz85vbm7W6xWI1LyUWc2EWYI0OeWccpNBpLXWUrTUOtdSZmZp2ybGKCLTPB/2h1N/KqWaqruVUvt+GKdBqy7cDQAR56bZbDbr9bpr25zTQkQAMLOIsIgwA1BVEclNs1qttpsNEe0PB601xggiVZ3G8XQ69cMwDsPp1O8P+/1ud7fbCcn1k+vt9qxr25zTommanHPbtl3X5dwEiRKiiOSUctfGmACIEAsHFg4iLCTUtO3lxcVmu9lsVzknItwjLNxhDjfHg/d+9ikevP25F4jw45/6UKn1f2z/IR587GufUCORpu2273nxpe/4zu95840PvO+V17rNepqG3d3tl5++fXf3bBxO8zzoPKnNpsW0ulYCcgg5pTY3KaUQhIQh7O6lVnMzENM9lhBCTE1edSuJwR2l1P40VLMQYkypaVtmqaqmqm4Ag4jBJETMAJVS94f9cDoO/SkwbTfb1GQiEpEcU26armtDSOaqZqaq5tXMqpurm5m7AcJExOYoVadpnqdhHKe+H/q+3x8O+/3+7vbu6+88u9vvD8fT7zz/3+PRc7/zfViYOVDKPI1zVQ0i5jZNk6rigbsDcHM8chgAM7zL4e4Gw8LheOBuAIiYQGAEWQRiDiIEAhPM1dTc8UCIiQmAm6ubm7k7ACIC4AtzAA7DP8MMC4e7GwwkHEMgYmJiIgIREzHTAgSAmfANbuYGN9WqZZomACGmJjdt28aYbj/4y3h08ZufIBFhCjG3bbs9O7+5uVmvVyBS81JmNRNmCdLklHPKTQaR1lpL0VLrXEuZmaVtmxijiEzzfNgfTv2plGqq7lZK7fthnAatunA3AEScm2az2azX665tc04LEQHAzCLCIsIMQFVFJDfNarXabjZEtD8ctNYYI4hUdRrH0+nUD8M4DKdTvz/s97vd3W4nJNdPrrfbs65tc06Lpmlyzm3bdl2XcxMkSogiklPKXRtjAiBCLBxYOIiwkFDTtpcXF5vtZrNd5ZyIcI+wcIc53Py9n32KB194/q0XfvptAESgn/jUB6daT8dhrgbE//35f4QH37//l5489+Ib7//g66+//+VXXtusN8M8HA+HZ7uv73Z3x/2uHw7T2Nc6wZWBwJRD7Lq2a5uubWNMwgygMkqt0zSqewwxhkVU87nMDo8xppTbVQeSMs3VHCQhhiY3xFRK1YWbg4WEmCCMB+Mw7g/7435/POyj8NXV5arriCWIsMQmp27VxRAdMDczB+COalpL0VqLqpniHjm4qv4r/8lfwIN//4f+7vF42u8Pt3f33rm93e0OX7z4u3jw3G9/3NwBMBGYAdRSxmkyVQJV1WmeTNXNATgMgBkWDgfgbjC8y+EA3A3fyg3mDoCJRISEAwvLggmEBw4HwMwEAtPCF2ZV1VTdHAAxAXBzhwEww4IZCwITEx65ucPMwAyRQMwAmIhAxEQgMAEgIjxwd9xzN6tmVecyzw4ESfn7/188Wn3xY24KCLEICYCmabZnF5cXl9dPbtarFZiqmdYKgJlCEAkhxhCCmNk4jvM0zePsVcHcte12u227NqdUaj0eDsMw1KoLMx3G8bA/zPPs+AZhYpEg90KIOacYkwRJMaacY4gsDPdSqz0IITRNs+pW27OtMPd9X2qNIQJeShmG4Xg6jcMwTdPp1O8P+/3usN/vROT65ub87CzntIgxhRhijDnntmlizDGmGFOQkNtm1XWpaZgpMHOUECSISEwxyGq1vrq+2mzWuYkhCt5FWLjDHG6OB+/97FM8+PLnXwDwkz/0oanWn2v+IR589OnHgSTSpLx68tyLb7z/g6+//v6XX3lts94M83A8HJ7tvr7bZULc/AAAIABJREFU3R33u344TGNf6wRXBgJTDrHr2q5turaNMQkzgMootU7TqO4xxBgWUc3nMjs8xphSblcdSMo0V3OQhBia3BBTKVUXbg4WEmKCMB6Mw7g/7I/7/fGwj8JXV5erriOWIMISm5y6VRdDdMDczByAO6ppLUVrLapminvk4KpaFvM8zfMwDMfjab8/3N7de+f2drc7nE6nL33bz+HRzT/9i0wEZgC1lHGaTJVAVXWaJ1N1cwAOA2CGhcMBuBsM73I4AHfDt3KDuQNgIhEh4cDCsmAC4YHDATAzgcC08IVZVTVVNwdATADc3GEAzLBgxoLAxIRHbu4wMzBDJBAzACYiEDERCEwAiAgP3B333M2qWdW5zLMDQVJKuetakXj8rv8Dj1Zf/LiQAGiaZnt2cXlxef3kZr1agamaaa0AmCkEkRBiDCGImY3jOE/TPM5eFcxd226327Zrc0ql1uPhMAxDrbow02EcD/vDPM+ObxAmFglyL4SYc4oxSZAUY8o5hsjCcC+12oMQQtM0q261PdsKc9/3pdYYIuCllGEYjqfTOAzTNJ1O/f6w3+8O+/1ORK5vbs7PznJOixhTiCHGmHNumybGHGOKMQUJuW1WXZeahpkCM0cJQYKIxBSDrFbrq+urzWadmxii4F2EhTvM4ebv/exTPPjC82+98NNvE2FBP/GpD07zvD/281Tdwq+89Ct48LGv/ejV9fOvvvbGq6+88cprb6y3m37sT4vj4XC43e3vTsf9OJxMC7NHkZRCm/Oq61Zt2zRNCAGAE0A0azn1A+A5t23TNG2j6vvjfpomMzRN3pxdpJS0ajVzcAiScgZxKaWqujkAYiEWZizUMY3D3e7uuN+fDocUw5Ob6/VmE0MgZgBBQs6NiGDBBHOICJO517lMZS5zLbW4ucMM7Gb/+n/2l/Hg3/vkf3k4HHf73e3t3e3d7m6/2x+O/+TyZ/Hg6jc/5rgXRIiZiVS1HwatFUBVnedZSwXgMABmWDjc3QC4OQB3c8M3I8a73GDuDgdAoBCERYRJJABETHjERMJCwgQC4HBTraqmaqYACAzAYWb4ZswgMAvjHuGeL8yUwCxMzACYCACBiIlA+NMcgJlV1aK1ltkBibH9/i/hUfrVP4d7LBSI2N3abn15dXVzdXPz5KZbrUDkcFUFnBfCwgwmJpRah74v82xFGRRi7Fbd+fn5erWKMVbV/nSapsnM3N3Mpmk6HI+lFGEGoGZMzCJuOk2zucV7KYg0TV6vNyknd1fVaRzNDEQpxpTzquvWmw0zD31fSpUgWnWap2kch3Gcxmmcxmmc+v50PB73+4OIXF1frdfrGCKLMFMQYZEUY845xsQcmEWYY4y5a3PKMYacU26bdtHk3Cza9Xbz5OZmvV5JZBHCuwgLdyzMHQ++/Uee4sGXP/8CgJ/8oQ9N8/xz3S/iwff+4UeZGg5ZpLm6fv7V19549ZU3XnntjfV204/9aXE8HA63u/3d6bgfh5NpYfYoklJoc1513aptm6YJIQBwAohmLad+ADzntm2apm1UfX/cT9NkhqbJm7OLlJJWrWYODkFSziAupVRVNwdALMTCjIU6pnG4290d9/vT4ZBieHJzvd5sYgjEDCBIyLkRESyYYA4RYTL3OpepzGWupRY3d5iB3azUe6WUvh8Oh+Nuv7u9vbu9293td/vD8Xjof/+l/wmPLn/zY0GEmJlIVfth0FoBVNV5nrVUAA4DYIaFw90NgJsDcDc3fDNivMsN5u5wAAQKQVhEmEQCQMSER0wkLCRMIAAON9WqaqpmCoDAABxmhm/GDAKzMO4R7vnCTAnMwsQMgIkAEIiYCIQ/zQGYWVUtWmuZHZAYc0y5aYKE4cNfwKP21z5GxO7WduvLq6ubq5ubJzfdagUih6sq4LwQFmYwMaHUOvR9mWcryqAQY7fqzs/P16tVjLGq9qfTNE1m5u5mNk3T4XgspQgzADVjYhZx02mazS3eS0GkafJ6vUk5ubuqTuNoZiBKMaacV1233myYeej7UqoE0arTPE3jOIzjNE7jNE7j1Pen4/G43x9E5Or6ar1exxBZhJmCCIukGHPOMSbmwCzCHGPMXZtTjjHknHLbtIsm52bRrrebJzc36/VKIosQ3kVYuGNh7t/+I0/x4AvPv/XCT79NhAX92CffHKf5sD8OY3GXX3/9/8aDj/z+D55fXb/88msvv/L6a6++3m22fX/qh37oj8fTfr+77ftDmUZzzVFyDCmnJqWubXJKKUZidjcQcwpmdhoHgFZdt1qtN9utuu1u7459P89zkLg9P885u8OxIJaYcwIwlaKqZg6AWYgEjIWbDfO03931h+M49k3Ozz25OTvbptwAmKexqtMCLELMTEQcYkoJhDqXaZ7naZrLXIuqmwNw/xv/9Q/jwd/8vr+zPxxu7+6ePbvd3R2O/el47P/J9c/iweVvfsxNAQ5BWISJTHUYh3kuAKpqKcVU8cDNATjMDA53NxjMFYAbzB2PmIgYCzeYu8MJxEQszCLCxCx4REQAiDmIEHMQAaBmrjaXYqoOMwMzFmZYOByPCCTMLAwQMQFwc8AXAIgIIADExEQACERM+NMcgJlVrUW1lgJARNpP/B4eyf/5EQDETBQI7LBVt7558tz1zc3N9XW3WoEZcDMDwEwg3HM3t1LKqT+5eo4pxZBibNt2vdk0TcPMqjqNY6mViJiImUut0zhW1Riiu03TVGt192mad/udVpUgKaYYwmq9Ojs7yzmr2TzN4ziYe4wxpxRTapum7ToAp9OplAL3Uus0jtM811JLme+VRZ2G4dQPIcjZ+VnO2c3NtKoSkTDHmHJOzGKKUrTUGYAESSnlnDfrzeX1+cXl1cX5+fb8bL1en59fXN9cbzYrFhIhvIuwcMc3e/EzX8GDL3/+BQA//gMfGKf557e/jAff9TsfEUmQhpDOr65ffvm1l195/bVXX+82274/9UM/9Mfjab/f3fb9oUyjueYoOYaUU5NS1zY5pRQjMbsbiDkFMzuNA0Crrlut1pvtVt12t3fHvp/nOUjcnp/nnN3hWBBLzDkBmEpRVTMHwCxEAsbCzYZ52u/u+sNxHPsm5+ee3JydbVNuAMzTWNVpARYhZiYiDjGlBEKdyzTP8zTNZa5F1c0BuFe1WnUu8zAM+8Ph9u7u2bPb3d3h2J+Ox/5wOv3BS/8zHl38xl8IQViEiUx1GId5LgCqainFVPHAzQE4zAwOdzcYzBWAG8wdj5iIGAs3mLvDCcRELMwiwsQseEREAIg5iBBzEAGgZq42l2KqDjMDMxZmWDgcjwgkzCwMEDEBcHPAFwCICCAAxMREAAhETPjTHICZVa1FtZYCQERSzCknklC/99fwKH7howR22Kpb3zx57vrm5ub6ulutwAy4mQFgJhDuuZtbKeXUn1w9x5RiSDG2bbvebJqmYWZVncax1EpETMTMpdZpHKtqDNHdpmmqtbr7NM27/U6rSpAUUwxhtV6dnZ3lnNVsnuZxHMw9xphTiim1TdN2HYDT6VRKgXupdRrHaZ5rqaXM98qiTsNw6ocQ5Oz8LOfs5mZaVYlImGNMOSdmMUUpWuoMQIKklHLOm/Xm8vr84vLq4vx8e362Xq/Pzy+ub643mxULiRDeRVi4410vfuYrePCF5996z8+8jQf0Y598c5zmw/44jEWVv/jmF/Hgu7/0/WfnN+97+ZVXX3391dfeXK83h+PheDoOw6k/7fe7u348lTILNOaUk+R4L6cYY8ghsggIzEwhKmyaRjO0bbNar88uLoLI8bToh34AaL3ZpJQJBLAzOIQmNwDGaa6q7gaAWAAGYeHAPI/Hw34aBislN8319dX52VluslY9Hg7DNNaiAIUUggiRxBiatiESU51KGcdpnudSZjczuLn/xH/7GTz4dz7+n+73+7vb3TvPnt3tjv0wHE+n37r5e3hw8Rt/wd2IwCxBhERMtR+GeZod7mpFq5vjG3wBwM0dpubuBoO5AnCDuTucQExEjHepOgAmIgaLMAkzCOwwPCIwCxNzEGERAjncVEup7mamZmDGwgwOdzcY7jGIWJiYhYiJCYCbA74AQET4BiImAExEIHwrYizMrGotWrSoAyLSfuL38Mj+t+8SZiYhDkQEoOtW19dPrq+vr66v16sVBSEiLIiECQT4PYNrLcM4CnjVtk3OLJJTattWQgCgqtM0mRkTCQuLADBVgzNzKaU/nYZxrKX2p9Nuv5vnmUVySjk368364vwiN1lVyzwPwwAgN01OSULIMeUmm3vf99M0meo0z33fz/PsjlrLPM+1VFWdpnEYBpF4eXmemwaPiCiwhBhDDEECnOa5nPp+nmcHWKTJabPZXN9c3VxfX11dbS8u1uv1+dnZ9ZOb7WYdcwiB8ScI7vhmL37mK3jw5c+/AODHf+AD4zT//PaX8eA7f/t7RDI4AeHs/OZ9L7/y6quvv/ram+v15nA8HE/HYTj1p/1+d9ePp1JmgcaccpIc7+UUYww5RBYBgZkpRIVN02iGtm1W6/XZxUUQOZ4W/dAPAK03m5QygQB2BofQ5AbAOM1V1d0AEAvAICwcmOfxeNhPw2Cl5Ka5vr46PzvLTdaqx8NhmMZaFKCQQhAhkhhD0zZEYqpTKeM4zfNcyuxmBjd3V69FJy390O/3+7vb3TvPnt3tjv0wHE/3/uB9fx+PLn7jo8wSREjEVPthmKfZ4a5WtLo5vsEXANzcYWrubjCYKwA3mLvDCcRExHiXqgNgImKwCJMwg8AOwyMCszAxBxEWIZDDTbWU6m5magZmLMzgcHeD4R6DiIWJWYiYmAC4OeALAESEbyBiAsBEBMK3IsbCzKrWokWLOiAiIcQQIrP4P/9FPAq/8ueJCEDXra6vn1xfX19dX69XKwpCRFgQCRMI8HsG11qGcRTwqm2bnFkkp9S2rYQAQFWnaTIzJhIWFgFgqgZn5lJKfzoN41hL7U+n3X43zzOL5JRybtab9cX5RW6yqpZ5HoYBQG6anJKEkGPKTTb3vu+naTLVaZ77vp/n2R21lnmea6mqOk3jMAwi8fLyPDcNHhFRYAkxhhiCBDjNczn1/TzPDrBIk9Nms7m+ubq5vr66utpeXKzX6/Ozs+snN9vNOuYQAuNPENzxrhc/8xU8+MLzb73nZ97GA/rJT31wKvPh0E9DKYpffeX/woPv/tInzi+uX3v1tVdfffON93+gW693t3f7w+503B9P+/1xN46D1ZkZMUoMwgQRCSI5xqbJOeUQIgV2QjWd5lnVAOpW7eXVVduuiDCVcjyeTDXGxBJowQvhEHJKIJ6nWU3NzbFgB+AGYmZo0VN/tFoY1DT5/Oxsve6appnn+WvvfP14OE7jDKLcZAmRCRJj27YxRDdU1XEa53nWqmoKdzP/8Z/9DB78u3/xb+/3+2d3d8/eeXa7Pxz74Xgaf/vJ38ODi9/4qKoCEInMFESq6jCOpRRf2AN3fBM3B/yeuZo53N1gcLi7VXUAQkQMIgbgbnjAJCzCDAI7zAwOxyMCCTOLBBFmBuDwqupmpuYwM7zL4a7mcHcjYgKRcGAhJiLGPV+YKQACExMAIsI9IiYmIhAAYsIDwj0zq1qLVrXiDiZpP/F7eFT/1w8xcQhRQhIWAE3Tbrfnl1eXV9c3m806xCAhELOIBGYWAkDMQdjgWquwrNsuxshEzEzMANys1FrnoqYAiDlIiDEws7mXufRDf9jvT6dTPwzjMBxPfSkzE6WUutVqs9lcXFzknAGUUsZhALBarUKMRBREYojqNo3jNM+1lH4YjofjNM9E0KrDMMxlXozj2J9OKeWbJzfn5+ddt1p1bdO2OSWWe8ycU25y5+77/b7v+2mezY2Zm7Y5vzi/OD/bnp2vuy537Wazvby82J5tN2erJic4Fk5YEOGbvefTX8GDL3/+BQD/5g99aCrz/9D+Ih58+HffIslAUI/nF9evvfraq6+++cb7P9Ct17vbu/1hdzruj6f9/rgbx8HqzIwYJQZhgogEkRxj0+SccgiRAjuhmk7zrGoAdav28uqqbVdEmEo5Hk+mGmNiCbTghXAIOSUQz9OspubmWLADcAMxM7ToqT9aLQxqmnx+drZed03TzPP8tXe+fjwcp3EGUW6yhMgEibFt2xiiG6rqOI3zPGtVNYW7matZLbXU0g/Dfr9/dnf37J1nt/vDsR+Op7E/nf7w5b+PR9tf/3MikZmCSFUdxrGU4gt74I5v4uaA3zNXM4e7GwwOd7eqDkCIiEHEANwND5iERZhBYIeZweF4RCBhZpEgwswAHF5V3czUHGaGdznc1RzubkRMIBIOLMRExLjnCzMFQGBiAkBEuEfExEQEAkBMeEC4Z2ZVa9GqVtzBJMwSJBAz/wu/hUfxCx8VFgBN026355dXl1fXN5vNOsQgIRCziARmFgJAzEHY4FqrsKzbLsbIRMxMzADcrNRa56KmAIg5SIgxMLO5l7n0Q3/Y70+nUz8M4zAcT30pMxOllLrVarPZXFxc5JwBlFLGYQCwWq1CjEQURGKI6jaN4zTPtZR+GI6H4zTPRNCqwzDMZV6M49ifTinlmyc35+fnXbdadW3TtjkllnvMnFNucufu+/2+7/tpns2NmZu2Ob84vzg/256dr7sud+1ms728vNiebTdnqyYnOBZOWBDhXe/59Ffw4AvPv/Wen3kbD+jf+uEPzXM5HvtpLKX6P37fr+LBd3/p+68unrz6xptvvvmBD7z/Q926e+fZO89un+13t4fD/tTvyzyBEIO0TYxRQGCAnYQlNznnFFMi4tmqmlazUss0TTGmi8uLs7OzVbc2+OF4nKcZICHhIMICZg4hp0TEpZaqCl9AHe7qBmKEEFR1niaYxSA5pa5tcpub3Izj8PTp093dbphmIVlt1zFGdw8h5KbJKRGJmo/TWEvVWs3N3N3sb/w3n8aDv/nxv73b7+/u9u88e3Z7t9+f+uPp9P8893k8OP/iR6uquwcWFmEmMx/HodQKc4fbwh0AEwEwdwBuDvjCTM3gcHeDwdxV1QEhYmECAXA4AAKRcGAhJgBmqubuRsTuBsOChIVJJAYRYgJgZqrmbm7uMDMsHO5q5uqGBTGYhEWEmZgAuLnDzLBgBoEBEBMAIgKImJiIQMREICyYAHezootSa3UYQdpP/B4eDb/w/hhDjCmmHCQRkHKzWW8vLi+vr69W63VIMTwQYSYGg4lIOKUoxABEwqptogQzd7ipmdtCq5ZaTLWqElEMIcaUcwIwjGN/Ou33+8PxOA7DOI7zg1ori6xX6/Pzs7Pz867tQgym2vc9EbVdF0IwVQAs4u5a6zTPZZ6Pp9Pd7d0wDMyk5tM0znOZ52nsh8PplGK8vr6+uDhfrdfbzXazWbdtG1MKIQhx23UX55csYej7aZ5V1eAAYgyrrm26tm3a3OSY0nq9vrq6Oj8725ytUop44IQFEb7Zez79FTz48udfAPBv/5V/bp7L55pfwIOPfOl7QcGRzOXq4smrb7z55psf+MD7P9Stu3eevfPs9tl+d3s47E/9vswTCDFI28QYBQQG2ElYcpNzTjElIp6tqmk1K7VM0xRjuri8ODs7W3Vrgx+Ox3maARISDiIsYOYQckpEXGqpqvAF1OGubiBGCEFV52mCWQySU+raJre5yc04Dk+fPt3d7YZpFpLVdh1jdPcQQm6anBKRqPk4jbVUrdXczN3NqlqtdS5lGPrdfn93t3/n2bPbu/3+1B9Pp/40/NEr/wserX7tewMLizCTmY/jUGqFucNt4Q6AiQCYOwA3x//PFbz+7J5e90H/rrWuw+90n57nmZm990wcT+LDOHYSxx2iBKoWiJGdwLwgQYgXFW+gCAUkDi1/AhQkeAEvEGAJKlQkQkWhcRsF1ViENqSppzm04pDWVEGTmd2Z/Rzv8+93XWst7uce75Hrzwd+YqZmcLi7wWDuquqAELEwgQA4HACBSDiwEBMAM1VzdyNid4PhhISFSSQGEWICYGaq5m5u7jAznDjc1czVDSfEYBIWEWZiAuDmDjPDCTMIDICYABARQMTERAQiJgLhhAlwNyt6UmqtDiMIsQizgfI/+ffwUvqdnw2SCEi5mQ3z1cXF1dVlPwwhxXAmwkwMBhORcEpRiAGIhL5togQzd7ipmduJVi21mGpVJaIYQowp5wTgcDzud7v1er3Zbo+Hw/F4nM5qrSwy9MNyuVgsl13bhRhMdb/fE1HbdSEEUwXAIu6utY7TVKZpu9vd390fDgdmUvNxPE5TmabxuD9sdrsU49XV1Wq17IdhPpvPZkPbtjGlEIIQt123Wl6whMN+P06TqhocQIyh79qma9umzU2OKQ3DcHl5uVwsZos+pYgzJ5wQ4WPP3nmOs3efvP3sG+/jjH75a1+YSjnux/FYS/Xf/vTfxtmX//6fvLx68tYXvvjWj33pi1/4Uj8MH15/eHP94sX1i/X6/jju4RpTGLp2Nps1bQ4MGKoq3IWYRSiIw8dSzI2Zx2lcb9YA5rP56mK1urwSkc12Ox6OtSqxdE1LQdxBIk1uwFRrdTNzd4OZVjM3JeYYEoBaJwKakEIkYZYgKaXdfvf8+fP7+4cylZzy6mKVcq6qAOUmx5BijGY2TVOt1czUHebm/q/8ys/j7N//p/78erO5f1jf3d7d3D3crTfrzfa7T/8Kzha//zPV1N2JKLCwiKmO01hLBZObVVU3B0BMANwcj/yRucPMYK4wnJirqgMQISYB45EBDCIWJpEAEOCmVk3djUkc7moOB0BMUaLEEEQIBMDM1M3NTM1hZnC4qZqauQNgIhYmJiZhxokZHO5uAJiEGScEJqYTgIiJiZiZQGA6AUCAuWnVqqVocXOA2q/+IV7af+tzMcaUc9N0MSRmzqmZzReL1fLi4mIYBolBRIhZmPDI1UxEcpNjDMwcJeSYmMjMtKq5nbg73Kuq1jqOo7kzc8556HuADsfD8XDY7/eHw/FwPGhVcxuP43qzNvXZbJjNZ6vlaj6f9X0PYLfbAWjbFkTj8aiqIgIiuJdayzQ9rNcvPnqx2+9w4m5mpVatuj8cNus1iObzedc2MaW2bYd+6Pqu7/rc5JzSfDa/vHx1GAZiSTE1XY4pMTERiAAGEYlITGk+m1+9+sp8PotJgrAbwHhEIML3e/bOc5y9/6tPAfybP//FqZS/2v0Gzr7y3beBYAhwubx68tYXvvjWj33pi1/4Uj8MH15/eHP94sX1i/X6/jju4RpTGLp2Nps1bQ4MGKoq3IWYRSiIw8dSzI2Zx2lcb9YA5rP56mK1urwSkc12Ox6OtSqxdE1LQdxBIk1uwFRrdTNzd4OZVjM3JeYYEoBaJwKakEIkYZYgKaXdfvf8+fP7+4cylZzy6mKVcq6qAOUmx5BijGY2TVOt1czUHebmrqq11nGa9of9erO5f1jf3d7d3D3crTfrzfaw27//mW/jpe533iaiwMIipjpOYy0VTG5WVd0cADEBcHM88kfmDjODucJwYq6qDkCEmASMRwYwiFiYRAJAgJtaNXU3JnG4qzkcADFFiRJDECEQADNTNzczNYeZweGmamrmDoCJWJiYmIQZJ2ZwuLsBYBJmnBCYmE4AIiYmYmYCgekEAAHmplWrlqLFzQECGI+o/er/i5fa3/vjMSRmzqmZzReL1fLi4mIYBolBRIhZmPDI1UxEcpNjDMwcJeSYmMjMtKq5nbg73Kuq1jqOo7kzc8556HuADsfD8XDY7/eHw/FwPGhVcxuP43qzNvXZbJjNZ6vlaj6f9X0PYLfbAWjbFkTj8aiqIgIiuJdayzQ9rNcvPnqx2+9w4m5mpVatuj8cNus1iObzedc2MaW2bYd+6Pqu7/rc5JzSfDa/vHx1GAZiSTE1XY4pMTERiAAGEYlITGk+m1+9+sp8PotJgrAbwHhEIMLHnr3zHGfvPnn72Tfed8cJ/ev/zOdK0fE4jcc6Fv3Om7+Lsy///T9xefnaZz731ltv/dhbb31xmA03J3fXL1682O42aiUKtV0zDP1iPsspEcPNylSsqrmDCMwOL1rNDELj4Xh7f6da+75fLFevvfpazPl4PIzH8ThO7mhS4piYKKSYU2YRN6+mblbVVYupO0BCKUYiMlUGojCzCIEIYGx3uw8//HC72Zp507QXFxcpN6rFgBBiDBJCAlBV3QzEAMzNVP/Uf/tzOPsPv/oXNtvNer2+vXu4u7+/vn142Gz/4NW/jLP57/90LWpudMIchM1RS6mqMDezqtXUiAkAEeH7+Im5w8zgcACuVqo6PAgxCRgfI2ICCTOLEJObu1ut6nBhAlBV3dzdiFgkxEeBRQjkcFdT0xNTc5iau5ubu5sbTohBxMTEJMw4MYPDAQQWYsJLRAQQMTERMxMITACICHB306pVa9Vi5gC1X/1DvLT71mdDjE1umqZLITFLbpvZbLFarpYXF33fhRhZmIgAd7eTqkqEFKMEYeYgIUogQE9qnUqBO4j4zMzKNKkqM6eU+mEQ5vGslDJO0zhOpUxadbff3d3em+swDIvF4uLiYrlYDMNAzPv9HkDbtkR0PBxKKTgjIlU9HA73Dw8fffTRbrfHGTOZeS3lOB732x0HWS2XMSWtKkH6fujatuu7rm2bphlm81euXp3Pl02T+7bvF0PbtCJCBHM1N1UVkZDSfDa/euVqPutJSJjcAAYIJ0T4fs/eeY6z93/1KYBf/tpbpeivDf87zr7y3a+YB0CAeHn52mc+99Zbb/3YW299cZgNNyd31y9evNjuNmolCrVdMwz9Yj7LKRHDzcpUrKq5gwjMDi9azQxC4+F4e3+nWvu+XyxXr736Wsz5eDyMx/E4Tu5oUuKYmCikmFNmETevpm5W1VWLqTtAQilGIjJVBqIwswiBCGBsd7sPP/xwu9maedO0FxcXKTeqxYAQYgwSQgJQVd0MxADMzVTLVEstx3HcHw6b7Wa9Xt/ePdzd31/fPjxstvvIKa5mAAAgAElEQVT9/r1P/zW81Hznp+iEOQibo5ZSVWFuZlWrqRETACLC9/ETc4eZweEAXK1UdXgQYhIwPkbEBBJmFiEmN3e3WtXhwgSgqrq5uxGxSIiPAosQyOGupqYnpuYwNXc3N3c3N5wQg4iJiUmYcWIGhwMILMSEl4gIIGJiImYmEJgAEBHg7qZVq9aqxcwBcseJG/qv/SFean/vj6eQmCW3zWy2WC1Xy4uLvu9CjCxMRIC720lVJUKKUYIwc5AQJRCgJ7VOpcAdRHxmZmWaVJWZU0r9MAjzeFZKGadpHKdSJq262+/ubu/NdRiGxWJxcXGxXCyGYSDm/X4PoG1bIjoeDqUUnBGRqh4Oh/uHh48++mi32+OMmcy8lnIcj/vtjoOslsuYklaVIH0/dG3b9V3Xtk3TDLP5K1evzufLpsl92/eLoW1aESGCuZqbqopISGk+m1+9cjWf9SQkTG4AA4QTInzs2TvPcfbuk7effeN9d5zQv/pPv6nVy1SORz0cy+985u/g7Mvf/ZOL5eWnfujNN9/8kc985vPDfLY/7Lfr9d36rkxjTNw0qe/atmtzk4OwmmmtZZpqra7uADGD4QR1G0s5HPYPDw+11pzTfDa/vHq16wcCpjLt9/txnMwtxti2fdc1OWfmCMDdxqlWLVoVcBIJ8oiJzN1NoRaYYw4ApmncbLd3N7fjNIWYutzOZrOYksLdDQARh5iYhBnEEkTABPNSyy/9l/84zv6Tr//3u91uvdncP6xv7h9ubh8e1pvfX/wKzvrf/ce01qoKczDFEAjkcFVTraXUUoq5EjGBmEFgYjoBCN/jOHP3WnWcJncnImEQMYHAIGJhYhYiJiY3B1y1AhAJ7m6mVdXNCUTCKcYUU4hBQgBgqqXWUoqpmileMoO5urm7AWASFhFmFgbg7kQkIgRyuLm7Oc6IiYkIxMwAHA7AzR2mJ65q6qam6L/2Hl7af+tzMcYQU05NjCmINF0/ny+Wy9V8sej7LuXMwgDMTE1Na6nV9JHBAAhzlEAOqzqWaTyOACRISqnvOhZxc3cDEGPs+l5YSi1mBndVHcdpt99t1pv1+mGz2QKYzxcXF8vLy6v5Yt62LRGNx5GFh65n4fFkmkoppgqiUsp+t79/eLi9uT0c9gCIKYZg7sfjUYuq6zDMnj59mlO6v79T865t265tmzbnFGPsun4+Xw3D0Oam6dqT3DYxpJiEhZgYgIikJvd9v1ythqGLMQgzMR4RQPgBz955jrMPvvkUwJ/+uR/V6r8+/02c/eQf/CQQgUiSFsvLT/3Qm2+++SOf+cznh/lsf9hv1+u79V2Zxpi4aVLftW3X5iYHYTXTWss01Vpd3QFiBsMJ6jaWcjjsHx4eaq05p/lsfnn1atcPBExl2u/34ziZW4yxbfuua3LOzBGAu41TrVq0KuAkEuQRE5m7m0ItMMccAEzTuNlu725ux2kKMXW5nc1mMSWFuxsAIg4xMQkziCWIgAnmpZbjOE3HcZzG3X6/2+3Wm839w/rm/uHm9uFhvdntdt999lfwUvibPwFzMMUQCORwVVOtpdRSirkSMYGYQWBiOgEI3+M4c/dadZwmdyciYRAxgcAgYmFiFiImJjcHXLUCEAnubqZV1c0JRMIpxhRTiEFCAGCqpdZSiqmaKV4yg7m6ubsBYBIWEWYWBuDuRCQiBHK4ubs5zoiJiQjEzAAcDsDNHaYnrmrqpqZwwBwn86+/h5fSuz8bYwoiTdfP54vlcjVfLPq+SzmzMAAzU1PTWmo1fWQwAMIcJZDDqo5lGo8jAAmSUuq7jkXc3N0AxBi7vheWUouZwV1Vx3Ha7Xeb9Wa9fthstgDm88XFxfLy8mq+mLdtS0TjcWThoetZeDyZplKKqYKolLLf7e8fHm5vbg+HPQBiiiGY+/F41KLqOgyzp0+f5pTu7+/UvGvbtmvbps05xRi7rp/PV8MwtLlpuvYkt00MKSZhISYGICKpyX3fL1erYehiDMJMjEcEED7x7J3nOHv3ydvPvvE+zuhP/9yPmto41XGs+930O5/5Ozj78nf/xDBbPH3y+utvfPrNN9+czxbjNI3T8TDu3a3JMefUdjnlGFjAcLOqWmt1czhATMwsRELVrJRpd9hvN5txmpi47drV8mLohxhidd/ttrvDfhzHIDJfLPquTynFmJipqk/TVMtUVUEUYwwnIsQMh5tqKUTIMVbV/WG73+93+z3Mc9O1TZObVoLghAA6YYKIEItIiDEGYVazUuo7/9lXcPafvvM/breb9Xpzd7++e3i4fdhuNuu/1fwFnA2/99PTOJmqmhKIgzAxAHObxqloMVUYHjGIWJgITEwAiAj/qKK1TJOqCxExiJhAAEhYmJiFRZjI3N3c3dycRQCYqpqZKwwkHEPIKccUYwwAqlottZSp1urmAIjpxNTUzFTN1Q0sLBJCECLGGTEJMTHZibubAw4QAGJiIgLhzGHm7mbqquZu6m5mGL72Hl7af+tzEkOMKcUmxRRCaJtutlzMZ4v5YtH1XW6yiKiZ24mqqdZaqpY6mSkcJ+ywqifjeDwcRlWNKeTcDMOQUzI3+AlSSl3fNTkTM8601uPxuF5vbm6u7x8e9rsdE88W88vLy9defW2xXLRty8y1FBbpuo6JSy3TyThWVWYu07TebNbrzd3d7fE4AggScpMAjMejqscoy+Xyhz71qRjjzYvrsUx91+ecUs45JRbJKefcNblJKcUUY0wxhZRSTLHJKaQUgqSYcpPbrp/Nhr7v2jaHEIjxiADCD3j2znOcffDNpwD+ta9+1tR+bfgbOPvx/+cnQBGIImmYLZ4+ef31Nz795ptvzmeLcZrG6XgY9+7W5JhzaruccgwsYLhZVa21ujkcICZmFiKhalbKtDvst5vNOE1M3Hbtankx9EMMsbrvdtvdYT+OYxCZLxZ916eUYkzMVNWnaaplqqogijGGExFihsNNtRQi5Bir6v6w3e/3u/0e5rnp2qbJTStBcEIAnTBBRIhFJMQYgzCrWSn1eDyOx/EwHvf7/Xa7Wa83d/fru4eH24ftZrPebPf/99Vfwkvh//hxNSUQB2FiAOY2jVPRYqowPGIQsTARmJgAEBH+UUVrmSZVFyJiEDGBAJCwMDELizCRubu5u7k5iwAwVTUzVxhIOIaQU44pxhgAVLVaailTrdXNARDTiampmamaqxtYWCSEIESMM2ISYmKyE3c3BxwgAMTERATCmcPM3c3UVc3d1N3MoOpwB/HiF97DS+E7P5NiCiG0TTdbLuazxXyx6PouN1lE1MztRNVUay1VS53MFI4TdljVk3E8Hg6jqsYUcm6GYcgpmRv8BCmlru+anIkZZ1rr8Xhcrzc3N9f3Dw/73Y6JZ4v55eXla6++tlgu2rZl5loKi3Rdx8SllulkHKsqM5dpWm826/Xm7u72eBwBBAm5SQDG41HVY5TlcvlDn/pUjPHmxfVYpr7rc04p55wSi+SUc+6a3KSUYooxpphCSimm2OQUUgpBUky5yW3Xz2ZD33dtm0MIxHhEAOETz955jrN3n7z97Bvv44x++euf16rTpIfjdNiXv/Xp38XZl/7gZ2f94pVXnj59/Y1P/fCnh2FeymRmHCBCIhyEObAwi+ATREwsIcQogWNkIrAr4KbjdNzud4fDoUwliHT90DZtblp32x8P+/1+u9sxaLFcdH2fUgohCktVLaXUWs2MmGNIMYUYo7AQDO5uqtXc63Eaj4fDOE2uJjH2XZ9SQ0JE7EAQyTkxS1VzQIhZJMbAzABK1a/9x1/C2X/+i3/54f7h9v7+5vb+fr3e7sfddvub4b/B2eL3f2YqU6nVqjqcmQG4edU6TqOqM4FA+BjjhIgBEAgAM76fmmstbiAGERMIH2MwSQhCxCIMwNzdzN2JGIC7mamauxuTxBhSTDHFGAOAqlZLraVUre4OgIgAuHutaqrmCoCIRYIwszBAgANETADcHHBTwxkxnQBETDhzc8AdVtXM1KGuro751/8IL+2+9VkJIYWUYo4pSYhd284Xq/l8McyGrm1DTiKiduKAAe5m7q6qOGGYajlM43gs0zSO435/qLWwSEq5aXIMQR85Mdqmnc1n89m867sgUko5HI+73e7h4eHm5mb9sN7udsy8nC8ury6ePXt2cXExzGYhhFIKMzdNw8SlTKXWWoqZhyCllM1m+7B+uL+7O44jE6WUur4XlnE8Asi5Wa6Wr7/+egzx7u52KiWnnJv8KCVmAQgOEJ+AyN2JSERyk4Z+6Ia267q2bXPT5KZtmzwM/TAbmpyI8YgAwg949s5znH3wzacA/o2f/4JW/dXmN3D25T/4SYU4AiCzfvHKK0+fvv7Gp37408MwL2UyMw4QIREOwhxYmEXwCSImlhBilMAxMhHYFXDTcTpu97vD4VCmEkS6fmibNjetu+2Ph/1+v93tGLRYLrq+TymFEIWlqpZSaq1mRswxpJhCjFFYCAZ3N9Vq7vU4jcfDYZwmV5MY+65PqSEhInYgiOScmKWqOSDELBJjYGYApep4OB7Gk8N2t3u4f7i9v7+5vb9fr7f7cbfdrjfbv7v6i3gp/M2fsKoOZ2YAbl61jtOo6kwgED7GOCFiAAQCwIzvp+ZaixuIQcQEwscYTBKCELEIAzB3N3N3Igbgbmaq5u7GJDGGFFNMMcYAoKrVUmspVau7AyAiAO5eq5qquQIgYpEgzCwMEOAAERMANwfc1HBGTCcAERPO3Bxwh1U1M3Woq6vDDXAH8eIX3sNL4bd/OqYkIXZtO1+s5vPFMBu6tg05iYjaiQMGuJu5u6rihGGq5TCN47FM0ziO+/2h1sIiKeWmyTEEfeTEaJt2Np/NZ/Ou74JIKeVwPO52u4eHh5ubm/XDervbMfNyvri8unj27NnFxcUwm4UQSinM3DQNE5cylVprKWYegpRSNpvtw/rh/u7uOI5MlFLq+l5YxvEIIOdmuVq+/vrrMcS7u9uplJxybvKjlJgFIDhAfAIidyciEclNGvqhG9qu69q2zU2Tm7Zt8jD0w2xociLGIwIIn3j2znOcvfvk7WffeB9n9G/9s18qauM0HffTbjf99g//bZx94f/66a4dLi9ffe3J62+88UbXz0opzJTbFCMTg4lYiJiESIRYTkIMKaTUpBxiijFBAIeTEXOxcjyM4/FwOB5MTUKMMTUpAzhMx+1ut11vAAzzWd91KTdBhEjUtJapVlU3YUkpxRRjTIEFDDJz91rLNI3H43EaR3OLIebcdG0fYjQ3g8MRQuqGNkiopVYzAMIsITAzgFr1q//RF3D2X/yLf/X+7v7m9vb6+vr2fr3Z7De77Xfa/w5n3bt/rGjVUtUNgBATk5nVWsdxqqpBhBgwfMzheIlAOGF8P1MFQMQEwpnDCUTCgYXlhAGYu5sDDhAAd3NzNXM4gWIMKaaYooTAhKpWS62lVFXA8YgAP6lVTdXh7kbELCJMzEJE7g6AiAC4u5ma4RPMIDAxneB73FyruZu6m5mbY/71P8JLu299VkJIIaWYY0oSYte2i+XFfLGYzeZd16acSAT+iAggwE9grkQgolrruD9M46RVx/F4OIyqBWAWiiHWqsfxUEoBkFOzXC5ms6HtOmEuUxmn8Xg87vb7zXqz3Wx2hwMT9UN/cXHx5MmTq8vL2XyeUzJ3Zs4pASj1kZkBCCKllPVms15vHu7vjsdRRFJKs9kgIUzTJMxN2y4Xi8vLSwAvrq+PhwMRSQht04QYRQTm41TNnJkBqKrBRaRtm8VisVwtlsvVfD7ruj63bZPTMPTL1aLJmRiPCCD8gGfvPMfZB998CuDf/ud+oqj9T+Gv4ezL3/2yKpmyQbp2uLx89bUnr7/xxhtdPyulMFNuU4xMDCZiIWISIhFiOQkxpJBSk3KIKcYEARxORszFyvEwjsfD4XgwNQkxxtSkDOAwHbe73Xa9ATDMZ33XpdwEESJR01qmWlXdhCWlFFOMMQUWMMjM3Wst0zQej8dpHM0thphz07V9iNHcDA5HCKkb2iChllrNAAizhMDMAGrVaZqO43Q4Hjbbzf3d/c3t7fX19e39erPZb3bbh83u7736P+Ml/htfVDcAQkxMZlZrHcepqgYRYsDwMYfjJQLhhPH9TBUAERMIZw4nEAkHFpYTBmDubg44QADczc3VzOEEijGkmGKKEgITqlottZZSVQHHIwL8pFY1VYe7GxGziDAxCxG5OwAiAuDuZmqGTzCDwMR0gu9xc63mbupuZm4ON8AdxItfeA8vhd/+6ZiShNi17WJ5MV8sZrN517UpJxKBPyICCPATmCsRiKjWOu4P0zhp1XE8Hg6jagGYhWKItepxPJRSAOTULJeL2Wxou06Yy1TGaTwej7v9frPebDeb3eHARP3QX1xcPHny5Orycjaf55TMnZlzSgBKfWRmAIJIKWW92azXm4f7u+NxFJGU0mw2SAjTNAlz07bLxeLy8hLAi+vr4+FARBJC2zQhRhGB+ThVM2dmAKpqcBFp22axWCxXi+VyNZ/Puq7PbdvkNAz9crVocibGIwIIn3j2znOcvfvk7WffeB9n9O/88z+p1cbjdNhP293xt954F2ef+7tfyalbra6uXnnltSev911fVZkpNykmCUISmIVjDDGGmGJOOeUm5bbNOeUmxSQxgNldQZAgRnC1sUzjcSzjVE1BFETMfJym/W67Xq9NrR/6tm1TSiyRCao6TVNVdTdhSTmd5JSYhZjd1FSnUqZpKqVUrYElt03XtLnpmLjWouYAYkqz2RBCKKWqqpmBwMTE5EBV/eqfewtn/9W/9L/c3929uLn+6KPrF9c3L65v7+7vvvvs13Bmv/F5PzEnJhaJEkgYgJY6TlM1JTxyNxgc7m5uODF3JgJAjI8RMV4iEACHu5sbiMEkLBJjIGYmAmDubk5MANzM3c3UDMwQCSmmEIOEQETurrWWUqqqm+N7/KRWNVWHAyAQGEzCDAI7DACBAThMzd0NJwYwiJhAzCAwMQEOwMyqqbs64ObmmH/9j/DS7lufDfEkpdikmEIIXd+vlhfL5Wq2WPRDl5s2xgAiBoHBBHM3U1N1OABT06m4ORHUrJaqpq5WTbXW7W53d3u/3+1KmWJMi+U8pqSqZgZ3M7AQ3KtqmaZxnACklPp+uLy6WC2Xs9ms7bp4IoFFAC+1mhkAJhKWsUzr9Xqz3tw/PJRpEpHc5KHvWUSrikjXtScxpf1+/8EHH9zf3e0PBwA5NzGGGCIcpSiAGBOIAAdTCLHvu6ur1eXV1ZPXXru8uJytVrNhaNt2NuuXq0WTMzEeEUD4Ac/eeY6zD775FMC/+4s/pdX+ov86zr7y3S9PCi1Q55y61erq6pVXXnvyet/1VZWZcpNikiAkgVk4xhBjiCnmlFNuUm7bnFNuUkwSA5jdFQQJYgRXG8s0HscyTtUUREHEzMdp2u+26/Xa1Pqhb9s2pcQSmaCq0zRVVXcTlpTTSU6JWYjZTU11KmWaplJK1RpYctt0TZubjolrLWoOIKY0mw0hhFKqqpoZCExMTA5U1TLVaZoOh+N6u7m/u3txc/3RR9cvrm9eXN/e3d+tN7uPPv8beKl++7PExCJRAgkD0FLHaaqmhEfuBoPD3c0NJ+bORACI8TEixksEAuBwd3MDMZiERWIMxMxEAMzdzYkJgJu5u5magRkiIcUUYpAQiMjdtdZSSlV1c3yPn9SqpupwAAQCg0mYQWCHASAwAIepubvhxAAGEROIGQQmJsABmFk1dVcH3NwcbnjEtPj6e3gpfOdnUkwhhK7vV8uL5XI1Wyz6octNG2MAEYPAYIK5m6mpOhyAqelU3JwIalZLVVNXq6Za63a3u7u93+92pUwxpsVyHlNSVTODuxlYCO5VtUzTOE4AUkp9P1xeXayWy9ls1nZdPJHAIoCXWs0MABMJy1im9Xq9WW/uHx7KNIlIbvLQ9yyiVUWk69qTmNJ+v//ggw/u7+72hwOAnJsYQwwRjlIUQIwJRICDKYTY993V1ery6urJa69dXlzOVqvZMLRtO5v1y9WiyZkYjwggfOLZO89x9u6Tt599432c0Z/9pa/UoofDdDge97vxN599B2ef+b2fiKmZzy8uV5eXrzxpu9bMRKRtU8wxRIkp5Bhjk3KTcspN27ZN2zRtSm1OSUKSIEQwGPgEYBEmcy8n0zSOY60KQFVLmQ7H42azMbN8knJKKQQBUKtO01RqgTsx53SSU0oiQgQzG6dJVc0cbiCOMbVNzk0TYwJQSjFVEMeUhr6TEN2sqpqqujNggDuq6lf/3Odw9l//qf/19u7u5vrmwxfXH3704h9++NH17e3/90O/jrPy7R81AzNEQpAQYhRhAFV1PI6qxQwOB+BqDnc3N5i7wwEQCGdMBIAYHyNid3ODAe5OIBGwSJTIcsIEcjjOzN3N3c1McSYS4pmEQER2olpLVVNzd3M8clNTM1N1OM4IRMIACORwAMJkBoe7GwwOxxmBwCBiAjGDwIA5UE39BObm5ph//Y/w0v5bn4sxppxzakNMMYa+7Reri9VyNV8uhmFouzamxMzEJwT4iZlVrW4KgAAYArOEwMx25u611HEat5vNzc3tw/rheByZaegHBzabzTSNbi4iMaaYQhBR9XEa3YyY+65bLBer5XKxWLRd1zRNEAFgZqVWuBOfEY3jeH9//7BebzabWkqIMaWUcxbmqppSms/nTdO4+3q9fv78+c317Wa7dvOUUwwpRBEWJgkhpZwkBBCIWJiatlku5xcXF5dXV5eXl6uLi/l8PgzDbD67WC2apiHGIwIIP+DZO89x9sE3nwL49/6Ft2vRX7Ffw9lX/sGXx8lKhVXE1MznF5ery8tXnrRda2Yi0rYp5hiixBRyjLFJuUk55aZt26ZtmjalNqckIUkQIhgMfAKwCJO5l5NpGsexVgWgqqVMh+Nxs9mYWT5JOaUUggCoVadpKrXAnZhzOskpJREhgpmN06SqZg43EMeY2ibnpokxASilmCqIY0pD30mIblZVTVXdGTDAHVW1Vp2m6TAed9vt7d3dzfXNhy+uP/zoxT/88KPr29v1w8Pdl34LL5Vv/6hICBJCjCIMoKqOx1G1mMHhAFzN4e7mBnN3OAAC4YyJABDjY0Tsbm4wwN0JJAIWiRJZTphADseZubu5u5kpzkRCPJMQiMhOVGupamrubo5HbmpqZqoOxxmBSBgAgRwOQJjM4HB3g8HhOCMQGERMIGYQGDAHqqmfwNzcHG54xLT4+nt4qfmdfyLEFGPo236xulgtV/PlYhiGtmtjSsxMfEKAn5hZ1eqmAAiAITBLCMxsZ+5eSx2ncbvZ3NzcPqwfjseRmYZ+cGCz2UzT6OYiEmOKKQQRVR+n0c2Iue+6xXKxWi4Xi0XbdU3TBBEAZlZqhTvxGdE4jvf39w/r9WazqaWEGFNKOWdhrqoppfl83jSNu6/X6+fPn99c3262azdPOcWQQhRhYZIQUspJQgCBiIWpaZvlcn5xcXF5dXV5ebm6uJjP58MwzOazi9WiaRpiPCKA8Iln7zzH2btP3n72jfdxRn/mF3+q1ro/TIfjtN+Pv/X0Ozj7kd/98RDyMMzni9XF6qptWxBCDG3XNE3KTcpNbrvmpG2b3DVtblNuYmpiTEECs4CYCWAYwUyZKcUYQiDiWsvhcJimqT7Sk1LLeBzNlJlDCCklZgGgqsfxWEpRVWaOIaaUYkpBBEBVHY8Hd885x5RyyiGmGKOIMJG5qyocIEgIMTcxSGBxR62lmrq5ual5Vf3qf/A5nP35f/l/e1ivb29vr69vPnpx/eFHL25ubv/Pq7+EM//rX3BzYhJiDpJCJGF3N9VxHGutZmoGh7sbDA43NXPHmcNxRiAATIQzc3c8csDhBCIgBhEJMQYRYWY3B+Bwc3czPzF3GIFZOEgIMYYYiMjdTbWWqqbm7uaAn5ipmpuqu+GMiAkExiMDGEQMwN1gcLi74YyICQQGEROIGQQ4oKYOONQdaph/7T28dPj252OMOTW56XLMKcWm6+eLxWK2mC8Xs9ms6bucEosIMwsT4HBTq1bdFAAzNyHFGHPKIoKXHG5qx+Ph/v5+s9lsdzt35JTG4/HF9YvtdlvVmCmdxBRicPPD8VCLAmi79vLiZLW6uBj6XkQAqJmplloBxBAAMrfD4XB3e7teb7bbrarmJosI/AQnbde++sorXdeZ+36/v7u9u3942G237pZzE1MKIk3OwzBvu75t2xgjMTu81soEiaFtcn8ym89ns/lycbJaLi4uV13bEuMRAYQf8Oyd5zj74JtPAfzZX/pjtdb/wX4dZ1/5B18ejzoWN6MQ8jDM54vVxeqqbVsQQgxt1zRNyk3KTW675qRtm9w1bW5TbmJqYkxBArOAmAlgGMFMmSnFGEIg4lrL4XCYpqk+0pNSy3gczZSZQwgpJWYBoKrH8VhKUVVmjiGmlGJKQQRAVR2PB3fPOceUcsohphijiDCRuasqHCBICDE3MUhgcUetpZq6ubmpeVUtRUuZxmnabXcP6/Xt7e319c1HL64//OjFzc3t/fr+/R/5Nl7yv/4FIeYgKUQSdndTHcex1mqmZnC4u8HgcFMzd5w5HGcEAsBEODN3xyMHHE4gAmIQkRBjEBFmdnMADjd3N/MTc4cRmIWDhBBjiIGI3N1Ua6lqau5uDviJmaq5qbobzoiYQGA8MoBBxADcDQaHuxvOiJhAYBAxgZhBgANq6oBD3aEGV8cjWvzCe3ip+/0/kWNOKTZdP18sFrPFfLmYzWZN3+WUWESYWZgAh5tateqmAJi5CSnGmFMWEbzkcFM7Hg/39/ebzWa727kjpzQejy+uX2y326rGTOkkphCDmx+Oh1oUQNu1lxcnq9XFxdD3IgJAzUy11AoghgCQuR0Oh7vb2/V6s91uVTU3WUTgJzhpu/bVV17pus7c9/v93e3d/cPDbrt1t5ybmFIQaXIehnnb9VtiNycAACAASURBVG3bxhiJ2eG1ViZIDG2T+5PZfD6bzZeLk9VycXG56tqWGI8IIHzi2TvPcfbuk7effeN9nNGf+cWfKrXuD9PxOO334289/Q7O3nz3ixJz28/mw3w+XzRtS8wpp2Fo267Nbdt1zdB3bd82XdPmNrVNzm1KKUgSCWBmEDFLEPX/nyt4+7Vty+7C/Gut9cu4zrnW2nvXZZfLiVIB2xDicqWAl3CJZQSJOU9AEkzCA4qSt0iAHeUlKDflIX8BUSkgIYISjCKisqxYIEeOL/hylDyYwAPBwZSrjl17r8tea845xui9t9Yy16yzTxX1fVprYUJKKcbELGZ6PB3XZSulqFYzN9Pa1LQBYOaYUhCBoWnbtrWeqTJRDDHGEGMiEQJUdVkWYhqGcRyGvh9jSswEMFwdcMcZMRExWGKQGBMTiqqpqqkbmmpV/ZH/9gu4+Jt/4RcfHx/vHx5ub+/e3N6+efP29u721/r/CRfhl36fmQEgJhYJIgRyeFMtpWhtagbA4QDcDQaHu5sbzszd4fgY4dscgDnOHCCACUE4hBRjCBKYGYDDzUzVALibmzuMwCwcJHCQGAIxw9zcWm1m5nBVczczVXNXM1c3mIMJZ8QgYgDuRsTEhG8xONzdcEHEAAgEBpMw41sU7maAmsMN0x//Gt5bfvb7Uko5913X912fUu6HcZrn3byfd/M0TV3fxxRZhJljECJiJjN3VzMDU5SQY8rxGYDW1N0AEHFK0dzXdd3W9Xg6qaqIrMvy5u3bw+HQagMgQUIIzKxNj4fDVoqadjm/ePHyxcsXr169GoYBgJvVWltt6kZEQQKApu10PN7e3j0+vjudFgC5y0xcSlFVAPNu/p7PfW6cJgC11m1dl3Xd1g1AzklCYOacu2mYh2Hshz6mxMIAtKmpAsaBJYTcdfM0zfv99dXV/vrq5nrf5UyMZwQQvsvrDz7CxTe++lkAP/6n/o3a2k/a/46LL/7GF7e11epaITH347ybdrvdvut7Yk45TVPfD33u+2HopnHox74buj73qe9y7lNKQZJIADODiFmCqGuthQkppRgTs5jp8XRcl62UolrN3ExrU9MGgJljSkEEhqZt29Z6pspEMcQYQ4yJRAhQ1WVZiGkYxnEY+n6MKTETwHB1wB1nxETEYIlBYkxMKKqmqqZuaKpVtZ6V1ko5Lcvj4+P9w8Pt7d2b29s3b97e3t3e3b/7p69/Cu/xL/xeYmKRIEIghzfVUorWpmYAHA7A3WBwuLu54czcHY6PEb7NAZjjzAECmBCEQ0gxhiCBmQE43MxUDYC7ubnDCMzCQQIHiSEQM8zNrdVmZg5XNXczUzV3NXN1gzmYcEYMIgbgbkRMTPgWg8PdDRdEDIBAYDAJM75F4W4GqDncoOpwB/H+3/ka3pt+/Y/0XZ9S7odxmufdvJ938zRNXd/HFFmEmWMQImImM3dXMwNTlJBjyvEZgNbU3QAQcUrR3Nd13db1eDqpqoisy/Lm7dvD4dBqAyBBQgjMrE2Ph8NWipp2Ob948fLFyxevXr0ahgGAm9VaW23qRkRBAoCm7XQ83t7ePT6+O50WALnLTFxKUVUA827+ns99bpwmALXWbV2Xdd3WDUDOSUJg5py7aZiHYeyHPqbEwgC0qakCxoElhNx18zTN+/311dX++urmet/lTIxnBBA+8fqDj3Dx4We+/PorX8cF/cU/9YOt6mlZ17UsS/sHn/k1XPxLH/6eEFLXT+MwDdPYd73E2Pd5nqZ+GoehH8Z+nMdx6Lsh565PuTuLuc8xSUgigYiDSEzB3ddtcTOWGISIuDY9HQ+n01JraapwU1NtTc0ACHNKiVkYaE23da2taFMQxRhCiDEECgKgNSulSJBxGIdx6PsxxgQCHO4GAjETCxPMYWbE0ueOmMxUzeEwN1WvTf/of/V5XPyt//hXDo+Hh8d393f3t3d3b2/v3t7d/Z/+P+Ai/coXTdXMABATMePMXE1L2ZoqEeM7uBsMZw53NzeYu8Nx5gDhmePMAQcczwhgQhBOKYnEGAMxE5Grqamq4Zk/MwfAwnLGEmIgIgCqZqpmBkDdTLU1NX1m7o6PEZ4xES6IwSR4z+EA3A3vETGBSJhAzDhTuJsB7m7NsPvjX8N7p7//u3OXu64fhyl3Q9floR/neT/N01nX9zElDsIgFo4xSJAgQsTMABMzRwmBRJjUbNu2w+FQtk3Nckq7/X4YhpwSgFKqqgKorW7LupZtW9fWGgBzd7NlWd+9e3c8HLZacs4vXrx89erlpz71qaEf9Fnbtq22ZmbMHEMwtdrq49PT7e3d4elxWzcwjcMIYFmW1iqA3X7/vZ///P7qioklSBAByNyYOKbIzACYmEhCSGchhhBEWMDMBDCIcBZj6od+t9tfX19Nu3ka+xgCMZ4RQPgurz/4CBff+OpnAfylP/1DrerfsZ/BxRf/vy8uS2vFWkMIqeuncZiGaey7XmLs+zxPUz+Nw9APYz/O4zj03ZBz16fcncXc55gkJJFAxEEkpuDu67a4GUsMQkRcm56Oh9NpqbU0VbipqbamZgCEOaXELAy0ptu61la0KYhiDCHEGAIFAdCalVIkyDiMwzj0/RhjAgEOdwOBmImFCeYwM2Lpc0dMZqrmcJibqtemtZazWstpWQ+Ph4fHd/d397d3d29v797e3d3e3f0/1z+J9/gXfi8AYiJmnJmraSlbUyVifAd3g+HM4e7mBnN3OM4cIDxznDnggOMZAUwIwiklkRhjIGYicjU1VTU882fmAFhYzlhCDEQEQNVM1cwAqJuptqamz8zd8THCMybCBTGYBO85HIC74T0iJhAJE4gZZwp3M8DdrRm8ucMdfP2jX8N7V//oh3M3dF0e+nGe99M8nXV9H1PiIAxi4RiDBAkiRMwMMDFzlBBIhEnNtm07HA5l29Qsp7Tb74dhyCkBKKWqKoDa6rasa9m2dW2tATB3N1uW9d27d8fDYasl5/zixctXr15+6lOfGvpBn7Vt22prZsbMMQRTq60+Pj3d3t4dnh63dQPTOIwAlmVprQLY7fff+/nP76+umFiCBBGAzI2JY4rMDICJiSSEdBZiCEGEBcxMAIMIZzGmfuh3u/319dW0m6exjyEQ4xkBhE+8/uAjXHz4mS+//srXcUF/8U//YCt6WtbTWrZV/8GnfxUX3/vhDwSJOY+5Pxty7rsu9WO/m+dpnoZpHKdhmsdhGLohp67Pucu5T12fUoohsQhAwiwsDqu1wF1EmAVAbXU5nbat1Fr0zLS12mpVVXcn4phSFAGgTbdtrbWaKohSjOEsBRF2kCpUlZhTzl3uctenlIQJxACYiYMQM4PUvTVlopw6YvIzgIgcUHNt+of+ymtc/M//ya89Ph0e3727e3i4u717e3v35vbtz9lfxUX8lR/U2tTNzQGIMC5UtdZmrkRMILzncADuBoPD3c0N6gbAHd/F8TECiCgGSTHFFCUEJgbg8FarquFjfgaAzpiDnAVmAmDm5uZmAJqqqdbWtNWmDjgcHyNcEAFMRAwiJhDeczgAd8MFERMIDCImAAwHHAZzc1P13Z/4Lbx3+Htf6Lq+74dp2HVD33ddP0y7eT9M0zQOXddJjCQMgJliOBMJIYiAIMIswkRkcNVS6+l4vLu/Px1PpZSU0tX11X6/3+12KSUABMKFw1tr27JWbXBvqtra4Xh89/Dw9HQopaSUbm5uXr588erVq34YWm21lmVdtTVzZyIJwd23bTs8Pb29vT0eDrU2ERnGAcDpeGqtAtjtdt/z+c/vdju4C0vMKUhgYRHJKbEEIrjDFHAwkQThIDHG3PU5pZhDEAbAIl3XD9O03+/maez6HEMgxjMCCN/l9Qcf4eIbX/0sgL/0Z36oFf079jO4+OI/+6HlVOpmTREk5jzm/mzIue+61I/9bp6neRqmcZyGaR6HYeiGnLo+5y7nPnV9SimGxCIACbOwOKzWAncRYRYAtdXldNq2UmvRM9PWaqtVVd2diGNKUQSANt22tdZqqiBKMYazFETYQapQVWJOOXe5y12fUhImEANgJg5CzAxS99aUiXLqiMnPACJyQM216baVs1rLsiyPT4fHd+/uHh7ubu/e3t69uX17e3v7D69/Eu/5z/+AmwMQYVyoaq3NXImYQHjP4QDcDQaHu5sb1A2AO76L42MEEFEMkmKKKUoITAzA4a1WVcPH/AwAnTEHOQvMBMDMzc3NADRVU62taatNHXA4Pka4IAKYiBhETCC853AA7oYLIiYQGERMABgOOAzm5qbqrjC4g25+9Lfw3vU/+pFu6Puu64dpN++HaZrGoes6iZGEATBTDGciIQQREESYRZiIDK5aaj0dj3f396fjqZSSUrq6vtrv97vdLqUEgEC4cHhrbVvWqg3uTVVbOxyP7x4enp4OpZSU0s3NzcuXL169etUPQ6ut1rKsq7Zm7kwkIbj7tm2Hp6e3t7fHw6HWJiLDOAA4HU+tVQC73e57Pv/53W4Hd2GJOQUJLCwiOSWWQAR3mAIOJpIgHCTGmLs+pxRzCMIAWKTr+mGa9vvdPI1dn2MIxHhGAOETrz/4CBcffubLr7/ydVzQX/73vlRLPZ62dSnbWn/xU7+Ki+/98PuJY0p96rocc+66oR+medjt52m3G6dhmqd5P4/TkLsud33OXcx9OotJQmASc7h5awVuTCxBQgjCDEBVt1JMVc1MW61lWdfT8VRrMXcmijGFIARY03XbWqnqJsQhxZRiTDGIOJE5mwEwM2eWGEPMXZdTTElYWIhEmJlI3K02BSBnxE7EzEECiN2ttfZv/hevcfE3/6NfORyeHt493t/f397e/vY3v/nm7e2vxL+OC/u57zdVh5mBGcxCxMTk5qrVDGfM+E5mcLi7wWCublB3uDvgAOEZAY5vIyIhCimm+IxFhJmIVK3Vqm64cHPAcUHMQYRFhBkXaubuMFfTWmrVqlXVzR3fiQAQCMRExCBiAoHxMcOZw/EdCAQGEZ4xuxmg5mjq+z/xW3jv8We+0HddP4zTOPXDOPRDPw7zvJ+meRrH3HUSAzPjjCgGYeELcoAIQmxudS1aa6n1dDy9efPm3eO70/FETLt53u33V/v9MAwxpZxSjJFFhFjdaq3uTkTurqrLsjw8PCynpZQSYri+ur6+vrq5ucldZ2q1lmVda61mxswxRrgvy3o8Hu7u75fToqoiMowDgHVZam0Apnn6zKc/nbtuOZ1aa0TEzCISY0w5pwthMYOrN20GZ3Aeut3F1X7OfRdEJJzF3PfzNI7jMExDjpEYzwggfJfXH3yEi2989bMAfvzf/3It9Sfr/46LL/3mlw7HbdvUFcQxpT51XY45d93QD9M87PbztNuN0zDN07yfx2nIXZe7Pucu5j6dxSQhMIk53Ly1AjcmliAhBGEGoKpbKaaqZqat1rKs6+l4qrWYOxPFmEIQAqzpum2tVHUT4pBiSjGmGEScyJzNAJiZM0uMIeauyymmJCwsRCLMTCTuVpsCkDNiJ2LmIAHE7tZaW7dStrJu5XQ6Hg5PD+8e7+/vb29vf/ub33zz9vb29u3/+5mv4r36s18wAzOYhYiJyc1VqxnOmPGdzOBwd4PBXN2g7nB3wAHCMwIc30ZEQhRSTPEZiwgzEalaq1XdcOHmgOOCmIMIiwgzLtTM3WGuprXUqlWrqps7vhMBIBCIiYhBxAQC42OGM4fjOxAIDCI8Y3YzQM3R1F3dHA568Sd/C+9d/aMf7odx6Id+HOZ5P03zNI656yQGZsYZUQzCwhfkABGE2NzqWrTWUuvpeHrz5s27x3en44mYdvO82++v9vthGGJKOaUYI4sIsbrVWt2diNxdVZdleXh4WE5LKSXEcH11fX19dXNzk7vO1Goty7rWWs2MmWOMcF+W9Xg83N3fL6dFVUVkGAcA67LU2gBM8/SZT386d91yOrXWiIiZRSTGmHJOF8JiBldv2gzO4Dx0u4ur/Zz7LohIOIu57+dpHMdhmIYcIzGeEUD4xOsPPsLFh5/58uuvfB0X9ON/9su11MNxWU9l2/QXX/0yLj7/4fcTh5S6GLNIyF0/jeP+are/2u/20zRP0zzt9nM/DV3XpdzHlGPsQggUIgHuMLVaymk5wa3LXc4ppxRCIGIQPqGq27Yuy+l4OKzbVmtlohgjMxOgtW3rVrWaGgvndJETi4AZIDNuqmVbzT3GmLtuGsfc9RxEWJgJzMLiDjtzB4FJQCQSYxQmMTdt7Q/9lde4+Bt/4Zefnh4fHt7dP9y9ub1/8+bN27dvfjX/DVzYz32/qToMFyKBmIUYQFMFHIC74zu4ucPU3N3c3NTUHe6ObyMAhE8QOAQWCTHGFCOLMNOZqqk2M8OFuQNwcwDEFERYRJiJCIC7q5m7W9PaaqmlFjU3x7cRQAAIBBIhIiYmAERMIFw4HIC74czwjHFGxAQ4w2Ewd7dm2P3xr+G9dz/zr3RdN3bjOM79OA591w/TNM/TtJvGoev7ECMLA2AiCXwGwOBuau5M1GrdTkstTVWPp+P93d27d4+HpycHuq4bx2k3T/049F3X9f0wDClGc7czVXcnZrir6rquT4dDLcXMc5dvrq/3+/3V1VXK2c22bTueTrUUdyfimKK7b+t6Op0e3r3b1tXMU0pd38PstC6tNgDDOLx69YqZH9+9Oy2LqYIoxZhSymddNwwDEZet1lJra3CH8DAMN9fXL16+fPHyZjdPMaYQA4fYpTSMwzCNu3nqciLGMwII3+X1Bx/h4htf/SyAn/ixP1BL/dvbT+PiS7/5pafjtm3NlYhDSl2MWSTkrp/GcX+121/td/tpmqdpnnb7uZ+GrutS7mPKMXYhBAqRAHeYWi3ltJzg1uUu55RTCiEQMQifUNVtW5fldDwc1m2rtTJRjJGZCdDatnWrWk2NhXO6yIlFwAyQGTfVsq3mHmPMXTeNY+56DiIszARmYXGHnbmDwCQgEokxCpOYm7a2bWXZSlnW43J6enp8eHh3/3D35vb+zZs3b9++ub29+6evfwrv1Z/9Ai5EAjELMYCmCjgAd8d3cHOHqbm7ubmpqTvcHd9GAAifIHAILBJijClGFmGmM1VTbWaGC3MH4OYAiCmIsIgwExEAd1czd7emtdVSSy1qbo5vI4AAEAgkQkRMTACImEC4cDgAd8OZ4RnjjIgJcIbDYO5uzeDNDe6gmx/9Lby3/4c/3I/j0Hf9ME3zPE27aRy6vg8xsjAAJpLAZwAM7qbmzkSt1u201NJU9Xg63t/dvXv3eHh6cqDrunGcdvPUj0PfdV3fD8OQYjR3O1N1d2KGu6qu6/p0ONRSzDx3+eb6er/fX11dpZzdbNu24+lUS3F3Io4puvu2rqfT6eHdu21dzTyl1PU9zE7r0moDMIzDq1evmPnx3bvTspgqiFKMKaV81nXDMBBx2WottbYGdwgPw3Bzff3i5csXL2928xRjCjFwiF1KwzgM07ibpy4nYjwjgPCJ1x98hIsPP/Pl11/5Oi7ox3/s95dSjof1dNpq0V98+cu4+PyH30ccY8wikTj0Xbfb766v9jcvb/ZX+3Eep3maduMwDKnLMWUJSSQwiQG1eT0rZVmWw+Mj4NM49MMw9H2Xc0zPckoSojA11W09LctyOi3rupyZmYQgzDBrtS3LUms1VyZOXepyTjmHEMEMsBm2Uk7HxeA5pX4Ydrt96nIIQsQAiOWMSQwGB9xBBOIgIYRIBAdK0x/+L1/j4q/9+V989/T47uHh7uH+/u7+9v7u/u7uH4S/jovwS7/PzBxu7gCE+JkIAHODucPdHIDDzd3NATc1NXO4q5mrqjsAd3yCCO8RwEIiEkRCTFECCRMIgMNN1c2JCYCbO9zcATARn4kIMxMDcPiZmZtqqWUrtZRqbo5nhGcEEIFATCQiJEwgZhCYmM5w4WfmDjODwwkEgBlnBjjcYTB3x/DHfhPvPf7MF3KXh24Yx7nv+m7ou/5snIZpnuduHHLOEgREzByEzty9aau1qjZ3r6Vup6XWCqCW8vj0dDye1uVk5iGGeBZSzimnNE7Tfr8XkVJKraU1NVN3qKk2raVstQDIOe/m3YsXN1fX1/vdLsTYWtuW9XA8bNtm7kwUYwJ8K2Vbt+PxUEoBICF0uVPT5bS0VkE8Dv3NzQ2Au7u7p6dDbZWJc5e73IUY+q6bpqk1vX17v66buYlwjGmYxpubm1cvXr789Kur3Zz7LuUcYuxyHoZxnser632XMzGeEUD4Lq8/+AgX3/jqZwH8xJ/7g6WUn1x/Ghdf+tqXHp+WsqobEccYs0gkDn3X7fa766v9zcub/dV+nMdpnqbdOAxD6nJMWUISCUxiQG1ez0pZluXw+Aj4NA79MAx93+Uc07OckoQoTE11W0/LspxOy7ouZ2YmIQgzzFpty7LUWs2ViVOXupxTziFEMANshq2U03ExeE6pH4bdbp+6HIIQMQBiOWMSg8EBdxCBOEgIIRLBgdJ029aybuu6Ho+nd0+P7x4e7h7u7+/ub+/v7u/u3t7e/ZNP/294j37h95g7ACF+JgLA3GDucDcH4HBzd3PATU3NHO5q5qrqDsAdnyDCewSwkIgEkRBTlEDCBALgcFN1c2IC4OYON3cATMRnIsLMxAAcfmbmplpq2UotpZqb4xnhGQFEIBATiQgJE4gZBCamM1z4mbnDzOBwAgFgxpkBDncYzN1hZtrcgasf/S28t/uH/1bf9d3Qd/3ZOA3TPM/dOOScJQiImDkInbl701ZrVW3uXkvdTkutFUAt5fHp6Xg8rcvJzEMM8SyknFNOaZym/X4vIqWUWktraqbuUFNtWkvZagGQc97Nuxcvbq6ur/e7XYixtbYt6+F42LbN3JkoxgT4Vsq2bsfjoZQCQELocqemy2lprYJ4HPqbmxsAd3d3T0+H2ioT5y53uQsx9F03TVNrevv2fl03cxPhGNMwjTc3N69evHz56VdXuzn3Xco5xNjlPAzjPI9X1/suZ2I8I4DwidcffISLDz/z5ddf+Tou6Md/7PfXUg6H9XTaatFffPnLuPieD7+POQTJLAHMXddfXe2vrq9f3Fzt9rthGqZpnOapH/ucc0w5xI5DEJbmXqtuW9ku6roy0zD0fe5ily+6vstD14UQANTaluVYylarrutyOh2bahAhkKnWUo6nU6nFtDFTyrlPXe5zDJEkqEObbbWe1g2goe+nadrtdylnIgbgbsQcQ2IR/AuEmUSEiNW1Nv2R//rzuPgf/8Off3x6vL9/uH+4v727e3t7d39392vd38BF+OV/HeYOB0AgEhZmCYGI/FvMVM3hz+zC3c1MTc3M1c1NzRyAOwB3nBERPsZEJGCWKFFiCCLETETuDnOHAyAQGDA43MwAEIjP5IyJGd9i7vCmuq3bVkvZqrk5nhFAAAEgEiIWJqYgQmAWBkiECURMbu5wc3czU8N7xHDAYQAM7gY39D/yG3jv8Pe+EFPuu74fpqHv+27IXT8M/TBO8zwPw5i7HGIEETPHwCCCuzbdWjFtZqatla1YVXcvpRwPh3VbS2mAsQiBHUbgEKTv+3k3hxC3ba2l1Nr0vdpqa02bxhiGYby6vnr18uX+6mocxpiimW3b9vT4uG2bqhJRTAmAtrZu27purVUAgSWmqGbLsrTWAPRdf3W1d/f7h4fT8aSqIpK7LCJuTkx91zW1w+GkTVNKOaeu74dxvDrb769fXE3TlLs+5xRT6rp+HPp5N19d74euI8YzAgjf5fUHH+HiG1/9LICf+HN/sJbyt9efxsWXvvalx3enUtSMmUOQzBLA3HX91dX+6vr6xc3Vbr8bpmGaxmme+rHPOceUQ+w4BGFp7rXqtpXtoq4rMw1D3+cudvmi67s8dF0IAUCtbVmOpWy16roup9OxqQYRAplqLeV4OpVaTBszpZz71OU+xxBJgjq02Vbrad0AGvp+mqbdfpdyJmIA7kbMMSQWwb9AmElEiFhda9OylXVby7odj8fHp8f7+4f7h/vbu7u3t3f3d8/+6eufwnvyS/8aAAKRsDBLCETk32Kmag5/ZhfubmZqamaubm5q5gDcAbjjjIjwMSYiAbNEiRJDECFmInJ3mDscAIHAgMHhZgaAQHwmZ0zM+BZzhzfVbd22WspWzc3xjAACCACRELEwMQURArMwQCJMIGJyc4ebu5uZGt4jhgMOA2BwN7jB3cxUHfs/8TW8N/76Hxn6vu+G3PXD0A/jNM/zMIy5yyFGEDFzDAwiuGvTrRXTZmbaWtmKVXX3UsrxcFi3tZQGGIsQ2GEEDkH6vp93cwhx29ZaSq1N36uttta0aYxhGMar66tXL1/ur67GYYwpmtm2bU+Pj9u2qSoRxZQAaGvrtq3r1loFEFhiimq2LEtrDUDf9VdXe3e/f3g4HU+qKiK5yyLi5sTUd11TOxxO2jSllHPq+n4Yx6uz/f76xdU0Tbnrc04xpa7rx6Gfd/PV9X7oOmI8I4DwidcffISLDz/z5ddf+Tou6D/7s7+/lPZ0OK1LKaX9wstfxsX3fPi7iUOImUkA7ob++vp6t99NZ+PQT/00jtM09sOQ+y53fdf1KXUSE5i0eWl1XQsBfd+lGEIQJgbAIjGGnFLOHQGllHVb1+VkqsxSq67rsTUNgd281bqu6+l42sqqrRIon3W577oYE0TMfCtta6pmMaXdPE/zbp6nEJOqmikcxJRSEok4IxJmAO44IyLA1ay29iP/zb+Mi7/253/+3dPTw8PD3f3dmzdvvvGN337z5s0/fvm/4oJ+4fcwM4EAkHAQkRBiEGYB4IDW1kz9zEzVTFVNVc3dzFTNXc3h7gbADeoOOEBEAIgBA0TALEFEYgwiBALgcAcYRCAIAQQ43M0ccAIRMwsLCQkTnrk7gFbbVrZnpZqZ4xkBBBCBQCLEJCwizCxnfEbMdAZy+DO7cHdzfJs73OEEOOCO+Ef/Cd47/f3flXLKqe+Goev6PvXdMm9vIAAAIABJREFUOEzj2TTPu77vY5dDCMQcWCRKECZmdzdVM3U44Zk2ret2Op2eDodaiplLkBSTuW3rVkupqkFkGIcYoqq2VmttrdVaW61lK0WbnqWcdrv91X5/c3Mzz1PKOaUUQ6ytPj4+rstSW2PiEIMwq2qptZRiTXHGHIOoWSlFW2uqKaZ5NzPx8XgotTJxDCH3nTZ9eLg/Hk9qlmKcp/18tttN8zRO09AP/dB3Xc45xRRDCDHG1PVn8zTNu+nqatflDMYZEUD4Lq8/+AgXX//qZwH85z/2B0tp/8v2U7j40m9+6fHptJXqJsQhxMwkAHdDf319vdvvprNx6Kd+GsdpGvthyH2Xu77r+pQ6iQlM2ry0uq6FgL7vUgwhCBMDYJEYQ04p546AUsq6retyMlVmqVXX9diahsBu3mpd1/V0PG1l1VYJlM+63HddjAkiZr6VtjVVs5jSbp6neTfPU4hJVc0UDmJKKYlEnBEJMwB3nBER4GpWWytbLdtZOZ6O756eHh4e7u7v3rx5841v/PabN28eHh4++ld/Fu+FX/p9AEg4iEgIMQizAHBAa2umfmamaqaqpqrmbmaq5q7mcHcD4AZ1BxwgIgDEgAEiYJYgIjEGEQIBcLgDDCIQhAACHO5mDjiBiJmFhYSECc/cHUCrbSvbs1LNzPGMAAKIQCARYhIWEWaWMz4jZjoDOfyZXbi7Ob7NHe5wAhxwh5q5qTnGP/bP8N7463+46/o+9d04TOPZNM+7vu9jl0MIxBxYJEoQJmZ3N1UzdTjhmTat63Y6nZ4Oh1qKmUuQFJO5betWS6mqQWQYhxiiqrZWa22t1VpbrWUrRZuepZx2u/3Vfn9zczPPU8o5pRRDrK0+Pj6uy1JbY+IQgzCraqm1lGJNccYcg6hZKUVba6oppnk3M/HxeCi1MnEMIfedNn14uD8eT2qWYpyn/Xy2203zNE7T0A/90HddzjnFFEMIMcbU9WfzNM276epq1+UMxhkRQPjE6w8+wsWHn/ny66983fGM/vKf/nIt9XBcTuvaiv7ip38VF5/78HeRBAmJSQyUu26/28/zPAzDNA7TNE7zdDaOY9d3ue/7ro+5TzmJBCM209o0hDCPU+4SwADclIhCiCFIlKCm67pty7Ksi5uKiBlqLa0pMVxVa1nX9Xg4rutStw1AzKlLOfc5xiQSFKhNqwPEOedpnqZx6odRgriqOc5EKKYkIgQGSIThUFM3nKmrq9amf+y/+wIuvvIf/B+Hp6d3j48PDw9v3rz92jc+evPN3/nHL/8uLuznvj/GIBJEmEWESURCiEwEZjertbmbmaq5qzVVc7Om6ubm7mamau5uANxgqoozxwWBwGCCiAQWjpGJGQTA4MwkzCDGBQEOfwYnAzFzkGfMDKgD7g60Utd13WoppZqZA0Rg0JkwmISEhYlZWESIOQgTizCBADhc1czN7cLdzfExNzjgAAHujvhH/wneW372d6eUcu5yN3Rd36WuH/pxmqZxmuf9MI1d38WQJPAzEWYWZrzHDCJOMZjqsqzr6XRaltpUCCwSQnD3ZVm2ZV23ramGIABaU2211lZqqbWW0rSV1tTdYozzPO+vrl+8uJmmKaaUYkwpq7bD09OyLLU1ACklYTb31tq6rq01cwSilBOAbSuttdpqjHGeZxFZ1s1NYwgASmvbshyeDqdtLaX0uXvx4tX19c1uN0/zbp7nYRxySvEsBQ7CLDHFlNI4DLvdbr/f7a72Q5fBOCMCCN/l9Qcf4eLrX/0sgJ/4M3+glvqT9tO4+OI/+6HDYdlKNWOSICExiYFy1+13+3meh2GYxmGaxmmezsZx7Pou933f9TH3KSeRYMRmWpuGEOZxyl0CGICbElEIMQSJEtR0XbdtWZZ1cVMRMUOtpTUlhqtqLeu6Hg/HdV3qtgGIOXUp5z7HmESCArVpdYA45zzN0zRO/TBKEFc1x5kIxZREhMAAiTAcauqGM3V11dp03UpZ13XbTutyeHp69/j48PDw5s3br33jozff/J272/vf+b6fw3vpV74owiwiTCISQmQiMLtZrc3dzFTNXa2pmps1VTc3dzczVXN3A+AGU1WcOS4IBAYTRCSwcIxMzCAABmcmYQYxLghw+DM4GYiZgzxjZkAdcHeglbqu61ZLKdXMHCACg86EwSQkLEzMwiJCzEGYWIQJBMDhqmZubhfubo6PucEBBwhwd9gzB9D/yG/gvfHX/3DX9V3q+qEfp2kap3neD9PY9V0MSQI/E2FmYcZ7zCDiFIOpLsu6nk6nZalNhcAiIQR3X5ZlW9Z125pqCAKgNdVWa22lllprKU1baU3dLcY4z/P+6vrFi5tpmmJKKcaUsmo7PD0ty1JbA5BSEmZzb62t69paM0cgSjkB2LbSWqutxhjneRaRZd3cNIYAoLS2Lcvh6XDa1lJKn7sXL15dX9/sdvM07+Z5HsYhpxTPUuAgzBJTTCmNw7Db7fb73e5qP3QZjDMigPCJ1x98hIsPP/Plz37l67ig//RHf7CWejwty1Zqs1/5ng9x8bn/+wugKCECAriE2HXdNE67ad7t9rvd/nq/31/t5mnKfZ9zDjGlmGKXY0oSA4kALCK560NMxCJMzCwixCIA3LS1rZRtW8u6tVbN1NwZMIe2atpcdV3X4/G4HI/LaXG3mGKMKXcphigxsgQTAQskBJHU5S6m2HUxCLOcsTwLLEGECCAC4OatqZmaq5s11Vrbv/3f/wAu/uq/+9PH4+l4ODw+HW7v73/7m29+55tv/6/xb+HCf/4HcsoppxiEWAAQwMxEBMDd1cxN3WGmav7MzN3N3OGupqaq5m5mqgaHu1s1dXW4A0RCIiwigYWISYhABAKTMIsICK7ucAIBcLjDoU5MHEKMIUggQFXNHea11m1dt7KVouoGgIlCkLPIwsLELMTERCASFmYW4TOCOexMtdVmZurm5oA/M3eYAY6PuXn/I7+B95af/b6cUuq6oRty1+fcnfVDP83z1f56t9uN85y7LggDZOa4YGYRZmFhCSIxBpjXUqq2M3NnXBCZmppupWzLsqzraVm2ZVnXbd3WUkorrWozMwLMvdXKIkPXX91cffrTn97vdjElCYGJVHVZ17JtpTYAOQYJgYlKrU9PT9tWWqspxmmehXlZ11KKqcaY9vtdCGHbNgO6lA6Hwz//5//8eFzGsY8xAQgShmHMXZ9S6vpumqZhHPuu78du6IfcpRhTCCHGOAz97urq5vrq5ua66zsGwCACCI5/wec++AgXX/vqZwH8pT/5Q7XUv5v/Hi6++Bs/eDitpTV1gKKECAjgEmLXddM47aZ5t9vvdvvr/X5/tZunKfd9zjnElGKKXY4pSQwkArCI5K4PMRGLMDGziBCLAHDT1rZStm0t69ZaNVNzZ8Ac2qppc9V1XY/H43I8LqfF3WKK/z9X8Npz3XrdB/0/xrgOc84151zrvu/tvR9rt0qaQ5M4PrRgKqRWdePYTQTaIPVNEf0GjSDhIL4AQpUQUqVK8KIuQRWqEC9ABKcIpMjBSpq26W7SQGoaQoK945297f0893Ed5ryua4zBupf9WMW/X4wpdymGKDGyBBMBCyQEkdTlLqbYdTEIs5yxPAssQYQIIALg5q2pmZqrmzXVWttpOZXlbF2W0+FwPOz3j0/7V3d3H377o299++W3v/3tb/6pX8Fr4z/7czEIsQAggJmJCIC7q5mbusNM1fyZmbubucNdTU1Vzd3MVA0Od7dq6upwB4iERFhEAgsRkxCBCAQmYRYREFzd4QQC4HCHQ52YOIQYQ5BAgKqaO8xrreuyrGUtRdUNABOFIGeRhYWJWYiJiUAkLMwswmcEc9iZaqvNzNTNzQF/Zu4wAxzf5eYA3A3A8MWv47X5dz+Xuz7n7qwf+nGadtureZ4305S7LggDZOa4YGYRZmFhCSIxBpjXUqq2M3NnXBCZmpqupayn02lZjqfTejoty7qsSymllVa1mRkB5t5qZZGh63fXu7feems7zzElCYGJVPW0LGVdS20AcgwSAhOVWp+enta1tFZTjOM0CfNpWUopphpj2m7nEMK6rgZ0Ke33+/fee+9wOG02fYwJQJAwDJvc9Smlru/GcRw2m77r+0039EPuUowphBBjHIZ+3u2ur3bX11dd3zEABhFAcHzX2+98gIt3X3z241963/CM/voXP1lrPZ1Oy1qr2m/+wG/h4uO//cNgCRIBNncmSTH1w7Ad5+326upqd727ur66msYx911MSUKIKXVdl/rc9X3MSWISiSBhFpYgIjFEFmECHKZaW6ml1FLWtbRSWi0AmAUwraW2Zk3LejrsD/uzw95aizHlnHJKMUcOMaYcu44kKoNJWCQESSnlnFLKMUYRYRJiCJ0xADNT1daamsLNzFW1lPLO3/wMLv7Lv/I/nY6n/eGw3x9uHx5fvbr/9suX/zD+XVzQr38i55xSFBGA3c3N8S9xmDncHYC7w9zhZwDM3Nysqbq5mak5zAAzr1rN3dUB4kDCchaEwcxEBAITgeSCAHNzgIhw5m7mqspMEmKMIUlgZjVTVVerta7Lspa1VHV3AkngICHFECQwM5jOcCHMTByiMAsRu1tT1dZqa9ZU3dzM1NTM4e4GAsD4Luu/8HW8tvzvP5ZSzqkbhj6nPucud13X99M07na7ebud5m3OnYgA0DNzAMIcYwwhxCAiIURhoKnCHEz4DnN1c3MAqlprOS3L/tlh//S4PxzXdVnPluKuRAJ4qVWYu77b7a5evHgxz1MMAUQATHUtpZZSmwJIMZwxc6318fFxWZbWWoxpu9sK835/KLUwS0ppM/QhBFUFICIPj0/vvfdeWcv1zfW4GYSFwOoGsBDlrtuMm34Y+mEYNsM4boah71KOKZJI3/dXV7vd1dXNzfUw9AQwgwggOP5/3n7nA1y89+WPA/j5v/zpWusvj1/BxWf+n08dlrU0rQ6wBIkAmzuTpJj6YdiO83Z7dXW1u95dXV9dTeOY+y6mJCHElLquS33u+j7mJDGJRJAwC0sQkRgiizABDlOtrdRSainrWloprRYAzAKY1lJbs6ZlPR32h/3ZYW+txZhyTjmlmCOHGFOOXUcSlcEkLBKCpJRyTinlGKOIMAkxhM4YgJmpamtNTeFm5qpaSlnW07qUdTkty+l0PO0Ph/3+cPvw+OrV/bdfvvzwW9/6f//E/4rX5t/5cyICsLu5Of4lDjOHuwNwd5g7/AyAmZubNVU3NzM1hxlg5lWrubs6QBxIWM6CMJiZiEBgIpBcEGBuDhARztzNXFWZSUKMMSQJzKxmqupqtdZ1WdaylqruTiAJHCSkGIIEZgbTGS6EmYlDFGYhYndrqtpabc2aqpubmZqaOdzdQAAY32UA3HE2fPHreG37tc/l1Ofc5a7r+n6axt1uN2+307zNuRMRAHpmDkCYY4whhBhEJIQoDDRVmIMJ32Gubm4OQFVrLadl2T877J8e94fjui7r2VLclUgAL7UKc9d3u93Vixcv5nmKIYAIgKmupdRSalMAKYYzZq61Pj4+LsvSWosxbXdbYd7vD6UWZkkpbYY+hKCqAETk4fHpvffeK2u5vrkeN4OwEFjdABai3HWbcdMPQz8Mw2YYx80w9F3KMUUS6fv+6mq3u7q6ubkehp4AZhABBMd3vf3OB7h498Vn3/rS+45n9HM//clS6+l0Wkqp6r/5g7+Fi4//9g85kYQEcFNn5hTT0PXTZtrOu5ur66ur65urq3kcY84pRQkh5ZSHYTOOm2noN5vU98RSqrmBOIYgLBJEmHBmqq1praWWUtelllJrg2sQgaG20mppra7Lsj8c9k9P9/f3WmrXd13uuj6nlEgkdn0/zhxkrdXciSjGOPR9P/RdP6QYiQhn7rgwM1Vt2lpTdyO4O9y01fpv/a3P4uJv/dv/w/FwPO4Pj/v9w9P+/nH/0e39r9PfwUX4jU+xnDGBzE3NXM3hANwcADEBIBCY8B3mABzu5urm5u7mgMPNzAE3VXc1c3cARCTMIoGFRYSZASIATEwkIgRygJiEGYCamWqtDUAQCSl2MUkQN5iptlbXuixLqaW1aoYgIlFSTFECizATADM3NyIKIiwSWFiEmRzQ2qq2Vptqq7WZajN1NVUFg0WIADCeWf+Fr+O19as/nmLKXdd3m67LKeWcu9x342aet9N2njfjnPuOiABoUzMHwCI5pZhiDDGIMBMRzpg5iBARADWzpgBIWJgBNNVa6/5pf39/9/j4tD/sj4fD/nAsawFgrqYqIQzDZrfbvvXmm0M/ADA4g5ppLaXUqk2JkFJiESaqtR4Oh3UtahpjmqbRHY8PD011sxlSjHTGHETcvbW2llJrCyLzPHVdJyK1tv3TodQqzCmlfrPph77r+s3Qj9M49H3uu5iSsOQuz9v5+mp388bNMAwEMAMEEL7P2+98gIv3vvxxAL/whU+XWn95/gouPvP7nzqs69q0uTuRhARwU2fmFNPQ9dNm2s67m6vrq6vrm6ureRxjzilFCSHllIdhM46baeg3m9T3xFKquYE4hiAsEkSYcGaqrWmtpZZS16WWUmuDaxCBobbSammtrsuyPxz2T0/39/daatd3Xe66PqeUSCR2fT/OHGSt1dyJKMY49H0/9F0/pBiJCGfuuDAzVW3aWlN3I7g73LTVelrXciqnclpOp+PheNwfHvf7h6f9/eP+o9v7b337o99/65fwWv/uv0Igc1MzV3M4ADcHQEwACAQmfIc5AIe7ubq5ubs54HAzc8BN1V3N3B0AEQmzSGBhEWFmgAgAExOJCIEcICZhBqBmplprAxBEQopdTBLEDWaqrdW1LstSammtmiGISJQUU5TAIswEwMzNjYiCCIsEFhZhJge0tqqt1abaam2m2kxdTVXBYBEiAIxnhu/i/gt/iNe2X/uprssp5Zy73HfjZp6303aeN+Oc+46IAGhTMwfAIjmlmGIMMYgwExHOmDmIEBEANbOmAEhYmAE01Vrr/ml/f3/3+Pi0P+yPh8P+cCxrAWCupiohDMNmt9u+9eabQz8AMDiDmmktpdSqTYmQUmIRJqq1Hg6HdS1qGmOaptEdjw8PTXWzGVKMdMYcRNy9tbaWUmsLIvM8dV0nIrW2/dOh1CrMKaV+s+mHvuv6zdCP0zj0fe67mJKw5C7P2/n6anfzxs0wDAQwAwQQvuftdz7AxbsvPvvml97HBf3c5z9Zaj2ejqe1NPN/8qd+GxcvfvsHnYQkAmTqRJxj7NIwjuNu3l3vdtdXV1e763kcc9+llFNOueuGzaafxnEa8tB3XQ/itaoZiEUksEgQFhIQzMxVW2u1llZKraXV6u4EuGpttdVSS1mX5Xg43j8+vPropbba9/3QDcPQp5QoSOy6zbTlKKe11NYIlFMap3EzjsMwxBhhUKipmhkAd29NW6um6uZ4Zmreavkr/9W/jou/+W/+98f9cX/YPz49Pe6Pd0+Hu/uHf8C/iIvwjz5NRO4OczNTNzcD4GfmAIiJiEWYQMREIIe7ubq5mak5zAxnzjhzc4cZYO5uhgtiDiIsEiUQMzHhgolEBCAWFmYWAWCqtdZSiptzkJRSTjnGSExetZyt67qstdbWCgAJMcWYYmI5YwCqZm7WlJhYJIhICDEIsQAw1abaam21qWlrrbZmaqZqgAgTExHO3DF88et4rX71J0KMuev6rs+pTznm3HVdvxnHad7O0zhMU84dE5lDq5obiIJIyjmlFM9CEKFADKYgIiEy4aw1bdoABJEQgoTAxGa6LMvj07P909P9w+PD/d3xcCyt1tJUNaYwjePu6upjb7zR9702Nbez2loppdVq7kwUYgwiAGqty7K2VgGEEPuhN9X7hwcm2l1dpZRKKcLc9T2A0/EIYLPZdH2fc04xEnMtdf+0X0tlIgkhdV16FnPuxmkY+mcxJRLpcpqmabfb3bxxPQwDAcwAAYTv8/Y7H+DivS9/HMAv/PSnS62/vP0KLj71e5861XVtrbo5CUkEyNSJOMfYpWEcx928u97trq+urnbX8zjmvkspp5xy1w2bTT+N4zTkoe+6HsRrVTMQi0hgkSAsJCCYmau21motrZRaS6vV3Qlw1dpqq6WWsi7L8XC8f3x49dFLbbXv+6EbhqFPKVGQ2HWbactRTmuprREopzRO42Ych2GIMcKgUFM1MwDu3pq2Vk3VzfHM1LzVsqxlXZd1WU6n03F/3B/2j09Pj/vj3dPh7v7ho5ev/u83fwmvhX/0aZibmbq5GQA/MwdATEQswgQiJgI53M3Vzc1MzWFmOHPGmZs7zABzdzNcEHMQYZEogZiJCRdMJCIAsbAwswgAU621llLcnIOklHLKMUZi8qrlbF3XZa21tlYASIgpxhQTyxkDUDVzs6bExCJBREKIQYgFgKk21VZrq01NW2u1NVMzVQNEmJiIcOYOIlxw/4U/xGvbr30upz7lmHPXdf1mHKd5O0/jME05d0xkDq1qbiAKIinnlFI8C0GEAjGYgoiEyISz1rRpAxBEQggSAhOb6bIsj0/P9k9P9w+PD/d3x8OxtFpLU9WYwjSOu6urj73xRt/32tTczmprpZRWq7kzUYgxiACotS7L2loFEELsh95U7x8emGh3dZVSKqUIc9f3AE7HI4DNZtP1fc45xUjMtdT9034tlYkkhNR16VnMuRunYeifxZRIpMtpmqbdbnfzxvUwDAQwAwQQvuftdz7AxbsvPvvml97HBf31n/pEKe2wHE5rbc3+6Y/8Di5e/PYPOhgcAWpqQpxi7Lt+GrfztNtN03a7vdpezdM4bjb9uNkMZ5t+HPLQp76LUUgCEZsTiImFWEKQwMIiTIQzd1M1re2Zmp5Va1pr01pLLeuyrsvxeDrd399/+8Nv1VKGzTBuxnHc5JRZOObcT1sOclpO1VTAue/meR7HcegHDuJNm7XW1N2I2N2aqramZ2Zwd7OmWmv7q3/7z+PiP/+Zv3c4HJ6e9o9P+4fHx/vH4+3Dwz/t/x4uwm98yuFurm5uDjieEeDPzFmYmIX4DEy4cLOmaqqtqbnC4LhguDmYQDhzwNzdDEAQ4SApJA7CRABUjZiCCIsIs4QQWAA001LKuiyqKiIppa7rU05BpNW2HE/ltJRaSq2mCkAkxBhTTkEEgJmrtqZqqm7OIjEGkRBTjCEQwdTVzZo2VVOt2ky1NTVVgzqeERgXm5/5Bl6rX/2JEGPuui71uetCjF3OXb8ZN+M0j9M0baYp546Y4V6bmjmAwJK7HGPKOcUUc4pRhC8kBCY207O1FHcPIiGEmJKwAFBTOE6n0+PT493t7cuXL+/uH/b7/XI8raXEKPO8vbm5/tibb/Z931qrta7PypmpAiCmFCOxmGpTrbW4Q4gkxpSSqe73+xjCx978WM55LSWwbHdbgPb7Jxa5ubkZ+gHf417VTM3NiYmDgAhADCF3ue+7oe9TTsSSuzyO43Y739xc931PADNAAOH7vP3OB7h478sfB/Dzn/9kKe3vX/0qLj75e588lXVVbW4OBkeAmpoQpxj7rp/G7TztdtO03W6vtlfzNI6bTT9uNsPZph+HPPSp72IUkkDE5gRiYiGWECSwsAgT4czdVE1re6amZ9Wa1tq01lLLuqzrcjyeTvf399/+8Fu1lGEzjJtxHDc5ZRaOOffTloOcllM1FXDuu3mex3Ec+oGDeNNmrTV1NyJ2t6aqremZGdzdrKnW2tbltKzPTqfT4XB4eto/Pu0fHh/vH4+3Dw93r+7+8E/8L3iNf/0n1c3NAcczAvyZOQsTsxCfgQkXbtZUTbU1NVcYHBcMNwcTCGcOmLubAQgiHCSFxEGYCICqEVMQYRFhlhACC4BmWkpZl0VVRSSl1HV9yimItNqW46mcllJLqdVUAYiEGGPKKYgAMHPV1lRN1c1ZJMYgEmKKMQQimLq6WdOmaqpVm6m2pqZqUMczAuOCmAAQcf+FP8Rr8+9+LnddiLHLues342ac5nGaps005dwRM9xrUzMHEFhyl2NMOaeYYk4xivCFhMDEZnq2luLuQSSEEFMSFgBqCsfpdHp8ery7vX358uXd/cN+v1+Op7WUGGWetzc31x97882+71trtdb1WTkzVQDElGIkFlNtqrUWdwiRxJhSMtX9fh9D+NibH8s5r6UElu1uC9B+/8QiNzc3Qz/ge9yrmqm5OTFxEBABiCHkLvd9N/R9yolYcpfHcdxu55ub677vCWAGCCB8z9vvfICLd1989s0vvY8L+rnPf3Jdy/50WNa1qv/Wj/wfuHjrt34QxODgTq2ZMKcYh36Yx6tpmudx3E7Tbrudp3map+/YjFO/6VOXOQqIzQ3ELIFZiIVZWDiQhCDMwoTvMFMzVTWYt1ZrLVpbbVrWZVlO62lZ1+XV7e0fv//+uqzjOM7TOE9z7jomCjmloQdLbdXNQ4zd0M/zdhz6lDMR11a06Zk5hAnEbmpmtTVTNTNVba0ta/l3/+u/iIu/8fm/uz8enp6eHh6f7h8eX90+3d0//PM3/kdc8K//pMPN3c0BiPAZgRxuZ+4AhPiMhIUZgJmbmzVt2mptpmqubjCGAwQwMQuBGYCbNVUAcsaSUgoxEAhAUyWmGAKLBJYQRSQCVquWWpbTqdYWRFJOfT/kLoUQW62n/WE5nGqrtTVTJRCLxBhijMQMwJo2baqtqcLAIiFIDDHEGGIIwubwMzVzUzNTbaqm2pqaq5mqgwlnBN78zDfwWvu1T8QY87M+pS7EmHIa+mHYjNM0z/M0TnPXdSwCR21qZgCYOacUY8o55ZSHvospuhsRBxEAZlprXUsxMwZxkBgTM8FdQui6zh37/f7+/u7ltz96dfvq/v7+8Wl/Op5YaJ7mmzduXrz1Vj8MrbVlWY6Hw/G0lHUxd2YJIhIEgKk2VXcQIcYURJjJzEspKaWbN276rqut5ZSvrq+CyOFwYJGb65uUkzZVUzwjgAkXBGYxQFXdjEwdAAAgAElEQVSZkHLqcur7XkJg4ZRSPwzb7Xxzc933PQHMAAGE7/P2Ox/g4r0vfxzAL/z0p9e1/P2rX8XFT37tE6e6rqbNAWJwcKfWTJhTjEM/zOPVNM3zOG6nabfdztM8zdN3bMap3/SpyxwFxOYGYpbALMTCLCwcSEIQZmHCd5ipmaoazFurtRatrTYt67Isp/W0rOvy6vb2j99/f13WcRznaZynOXcdE4Wc0tCDpbbq5iHGbujneTsOfcqZiGsr2vTMHMIEYjc1s9qaqZqZqrbWlrWsy+m0LOuyLqfT/nh4enp6eHy6f3h8dft0d/9w//j4/g/9Cl7zX/sJNwcgwmcEcriduQMQ4jMSFmYAZm5u1rRpq7WZqrm6wRgOEMDELARmAG7WVAHIGUtKKcRAIABNlZhiCCwSWEIUkQhYrVpqWU6nWlsQSTn1/ZC7FEJstZ72h+Vwqq3W1kyVQCwSY4gxEjMAa9q0qbamCgOLhCAxxBBjiCEIm8PP1MxNzUy1qZpqa2quZqoOJpwRmJiIAEj/hT/Aa9uvfS6lLsSYchr6YdiM0zTP8zROc9d1LAJHbWpmAJg5pxRjyjnllIe+iym6GxEHEQBmWmtdSzEzBnGQGBMzwV1C6LrOHfv9/v7+7uW3P3p1++r+/v7xaX86nlhonuabN25evPVWPwyttWVZjofD8bSUdTF3ZgkiEgSAqTZVdxAhxhREmMnMSykppZs3bvquq63llK+ur4LI4XBgkZvrm5STNlVTPCOACRcEZjFAVZmQcupy6vteQmDhlFI/DNvtfHNz3fc9AcwAAYTvefudD3Dx7ovPvvml93FB/95Pf2op6+PTfilrqfbPfux3cfHWuz/gTMTRQa25COeYhm6znXfTOE2bcZ7G3byb53k7z+N2PhvHKQ99TMEZzbxqBTjFFCSAmUlEiJ9JECESYRDhzB0wAKaqtbbWqqqWdV2Op2U5lbK+evnqj/7oj06n02YzbOd5u9v1fS/MJAwRMAMUU+y7vh/6zTh2KRELYGspVRvMQCwhBREQ4Ki1tKa11dZaWcuyLn/tF/8SLv7Tz/3i8Xh4fHq6f3h6dXv34Uevbu8e3/uB/w0X9tUfBxwgYgoiMUYJgYjcXVtTM3cHwMQiTMw4M3d4bc2a1lZra9pqU3fAAQJYKIRITAAcXmsFQMxBJKccYmBmN2+qxBRDDEFYOEqUGNxctS5rWU4nVWXi3OVh6HPqJAatZf+4X46nWmozNVUALCJMIgEgwE2tmZqqqQEghoSYYgwSQowiTCAADj8zc3Nzs6Zqqq2pajMYAAIT0/DFr+M1+41PpRhTyjn3MaQYY+pS1/XDZtzO8zRv5+227/sYI4Da7DuYKMTv6rtuGjchiKma48zdTLXUWta1qbo7ETGRuWtrueturm9iSrWW/X5/f3f36vb21cuXd/cPj48PMMzb6er6+sWLF5thY6bHw/H+4X5/OJZS3C2GKEGCiDtaq2bOTCHElBIzuTsucs7TNOWuiyEOw7C72uWYqjYAKSUisqYGZ5ABps5EEmIIElIiJm0NRClKjDHlzMLmiEG6rt9up5s3boZhIIAZIIDwfd5+5wNcvPfljwP4D7/wmaWsv7z9VVz8xO/++LHVYmrmzkQcHdSai3COaeg223k3jdO0Gedp3M27eZ638zxu57NxnPLQxxSc0cyrVoBTTEECmJlEhPiZBBEiEQYRztwBA2CqWmtrrapqWdfleFqWUynrq5ev/uiP/uh0Om02w3aet7td3/fCTMIQATNAMcW+6/uh34xjlxKxALaWUrXBDMQSUhABAY5aS2taW22tlbUs63I8HpdlWdf1dDwdj4fHp6f7h6dXt3cffvTq9u7x6Wl/98l/gNfsqz9OTEEkxighEJG7a2tq5u4AmFiEiRln5g6vrVnT2mptTVtt6g44QAALhRCJCYDDa60AiDmI5JRDDMzs5k2VmGKIIQgLR4kSg5ur1mUty+mkqkycuzwMfU6dxKC17B/3y/FUS22mpgqARYRJJAAEuKk1U1M1NQDEkBBTjEFCiFGECQTA4Wdmbm5u1lRNtTVVbQYDQGBiIgLAzJI///t47epffD6GFGNMXeq6ftiM23me5u283fZ9H2MEUJt9BxOF+F19103jJgQxVXOcuZupllrLujZVdyciJjJ3bS133c31TUyp1rLf7+/v7l7d3r56+fLu/uHx8QGGeTtdXV+/ePFiM2zM9Hg43j/c7w/HUoq7xRAlSBBxR2vVzJkphJhSYiZ3x0XOeZqm3HUxxGEYdle7HFPVBiClRETW1OAMMsDUmUhCDEFCSsSkrYEoRYkxppxZ2BwxSNf12+1088bNMAwEMAMEEL7n7Xc+wMW7Lz775pfexwX9+1/41FrK/ePTaVlK09/58a/h4q13f8CZiKOD1FxYupD7YdiO22nazpvNvJ2vtrv5bJrGaRrHcRg2eeg5Cgk5vKqCKEoKIkQMJiYWJn4mxCTMREIEBhFAYHetTZtWNy9rORz2y3Iqpdze3r739W8cD/vc9/M0XV9fb4YNC4NgbsQcU0xdP26GftjkLscQ4aiqtZam1d2JJIQgIUoQd7RWtbXatJRSSzkty1/7xb+Ei//sp/6b/X5///j08PDw0cvbP/7wo1d3Dx/+6K/iwr76YwCIOchZ6LokEpnJzEst2pqaARBmOgOBCRem2lRrrVpbrbU0NZg7iMDEMUUKBMDNa624iDGmlGKMzOzmTZWYUkpRAgkLk0gErFYttSynxUyZJaW0GYaUEgdptR4fnpZlqaU2U1dzODERsTABMIO5wuBwdwNAxCwSwzMJgYmZCYCZO/wM5mbWVE21mZmrmxMTEc76L3wdr/E//jMhxpRyzn08CzGddf04jPN2muftvN31myHGSGBVU1NTBVEMIT5LfZ+HoQ8stVVTNXN3a6q11nVdW2tuDjgAM6ut5ZRv3rgZhgFALWW/39/d3796+fL29u7u/s5UN8Nmd3391ltv9n3fWjsej3d3d8fDodQGIMUgZywGb7UCkBBjDDl3QbipESGllM9SyrkbhmEcN+M05ZSZ2czU1MwBECFIAFBLAxBTyimnnMDcWnV3YopBYkos7I4QpO/7eTvf3NwMQ08AM0AA4fu8/c4HuHjvyx8H8B998TNrKf/z/BVc/On/808vrRRVBzsTcXSQmgtLF3I/DNtxO03bebOZt/PVdjefTdM4TeM4DsMmDz1HISGHV1UQRUlBhIjBxMTCxM+EmISZSIjAIAII7K61adPq5mUth8N+WU6llNvb2/e+/o3jYZ/7fp6m6+vrzbBhYRDMjZhjiqnrx83QD5vc5RgiHFW11tK0ujuRhBAkRAnijtaqtlabllJqKadlWY6n47KUsi6n036/v398enh4+Ojl7R9/+NGru4fj8fD4md/Ea/5rPxHkLHRdEonMZOalFm1NzQAIM52BwIQLU22qtVatrdZamhrMHURg4pgiBQLg5rVWXMQYU0oxRmZ286ZKTCmlKIGEhUkkAlarllqW02KmzJJS2gxDSomDtFqPD0/LstRSm6mrOZyYiFiYAJjBXGFwuLsBIGIWieGZhMDEzATAzB1+BnMza6qm2szM1c2JiQhnxEKASAif+z28dvP7X4xnIaazrh+Hcd5O87ydt7t+M8QYCaxqamqqIIohxGep7/Mw9IGltmqqZu5uTbXWuq5ra83NAQdgZrW1nPLNGzfDMACopez3+7v7+1cvX97e3t3d35nqZtjsrq/feuvNvu9ba8fj8e7u7ng4lNoApBjkjMXgrVYAEmKMIecuCDc1IqSU8llKOXfDMIzjZpymnDIzm5mamjkAIgQJAGppAGJKOeWUE5hbq+5OTDFITImF3RGC9H0/b+ebm5th6AlgBgggfM/b73yAi3dffPbNL72PC/r5L35mXZe7x6fDclqL/u4n/i9cvPnunwQLcQSxmTOHLndDN0zDOE3bq93uere92l3P0zRshtx1Xc6p63LXpy7FHDhEEmJiIjAIxEwgIpwRM4FJQCR8JsQkxAQCYKZqDreylv1hv57WtZTbu1fvfeMbT4+PEsJms3nj5o3NOIYggDdtItwN/eZsGLq+k5iYoGpNVVvTM8MzkRAkxgSCNjXVZlZrq7WWdf13/s5fxMXf+MJ/+/j09Pjw+Or+4aOXLz/88KNXd3cf/uhXcWFf/XFiCnIWYoopphgDAVV1XddaqrnBHEzPQMzExGACYKpNtdZa1lJbqermTgAzxyQchIhdrWpzMwBBJOUcYhRiAE0bMeecQxAATCLMDmuqtbZ1WQEPElJOXdfFEFmolHJ6Oq7L0p6pucLwjPFdhmcMIhYmgAFjlhCCsJAwgRx+BnMzc7i5m6qZNlU3kABgAMREQP78H+C1+O5nY4w5p5T6GKOEGGPKXd4Mm2nezvN23m2HYcg5M3FrpmemRBRCiDHmnFOMKQYAtRYzg7m6mWqtdV3X2pqZASAidzdVCWGz2XS567oMYC3lsN/fXdze3tbaur7fztPN9U3Mqa7leDo+PT0dT6fWFECKQUIkgju0VSKOIeSuyzkxs6pKCMMwpJSIKKe0Gcdh2HRdTinFEM10LcXMQogxhL7v3LEsBY6YUs4ppOTAupxaUwAhUIxRQpAgOXfjOM7zdHW1zV1HADNAAOH7vP3OB7h478sfB/Af/+U/u67LL81fwcWP/s4PL7UVNyeAhTiC2MyZQ5e7oRumYZym7dVud73bXu2u52kaNkPuui7n1HW561OXYg4cIgkxMREYBGImEBHOiJnAJCASPhNiEmICATBTNYdbWcv+sF9P61rK7d2r977xjafHRwlhs9m8cfPGZhxDEMCbNhHuhn5zNgxd30lMTFC1pqqt6ZnhmUgIEmMCQZuaajOrtdVay7qelmVd1rPj6fT49PT48Pjq/uGjly8//PCjV3d3x+Pp8TO/idfkNz4pEmKKKaYYAwFVdV3XWqq5wRxMz0DMxMRgAmCqTbXWWtZSW6nq5k4AM8ckHISIXa1qczMAQSTlHGIUYgBNGzHnnEMQAEwizA5rqrW2dVkBDxJSTl3XxRBZqJRyejquy9KeqbnC8IzxXYZnDCIWJoABY5YQgrCQMIEcfgZzM3O4uZuqmTZVN5AAYADERACfEZMI/4Wv4bW3/vBnY4wSYowpd3kzbKZ5O8/bebcdhiHnzMStmZ6ZElEIIcaYc04xphgA1FrMDObqZqq11nVda2tmBoCI3N1UJYTNZtPlrusygLWUw35/d3F7e1tr6/p+O0831zcxp7qW4+n49PR0PJ1aUwApBgmRCO7QVok4hpC7LufEzKoqIQzDkFIiopzSZhyHYdN1OaUUQzTTtRQzCyHGEPq+c8eyFDhiSjmnkJID63JqTQGEQDFGCUGC5NyN4zjP09XVNncdAcwAAYTvefudD3Dx7ovPvvml93FB/8HP/NnTabl7fNifTmtt//wT/wIXb777J8FCkgB2d6YwdF3f9Zth3k7z9dnV7ubqZh6nbui7LocQYoox55hT7FKIkYMQADPAGUTMTAAIzxgEJiGmwCwSWESYiQgOdyeiUsr+cDydTmVdbu/uvvnNbz7c39fW+q5/42Mfm8YppigM1RYCbzZDv9kMfR9TYiYAZq525qpqbs3cDMQUU2JmM4O5g9Rcm661/NW//edx8V/87H93//T0cH//6vbuo5e3H3zro/v7+2/+0FdwQb/+CWYWCSGGFGNKiUUAaGunZWm1qpm744LOQEEETGfubqq11nVZS62lqboBEOaQRIIQsbtp0zMAQSTlLBcAVE2Ec84sDMMzBgzm1lqrrQGIIaSYcpdYBICWdjwc67q2Z2quMJw5HBcEAoNJQhAWYSJzBxBEiJmIAPiZXbi7ubuZaVM1VQAsAmJiAkBA/vwf4LX0W/9aOos55S6GGIKEkHLfb4ZhnOZ5nuZ512+GLmcQq5qpqhkRxRBTTjnlGIMQ3K2pmhkAU3O3WutpWVprbgaiGAIzAyDmEEJOKXedMNfWjofD/cXt7W2pLac0juNutxWR4+l0Op5Oy6mUYqogiiGyiBAZXFWZJed01uUsIqoqIWw2mxgigJjiOGxyl1meMbOZ1VLMPMSQUuq7DpBaCkA5pdR1MSaHLetaSwGMhHNKMaUQ0mbst9vtPM/TPOWcCGAGCCB8n7ff+QAX73354wD+k5/9V0+n5ZfmX8HFj/zODy+1VTcngIUkAezuTGHour7rN8O8nebrs6vdzdXNPE7d0HddDiHEFGPOMafYpRAjByEAZoAziJiZABCeMQhMQkyBWSSwiDATERzuTkSllP3heDqdyrrc3t1985vffLi/r631Xf/Gxz42jVNMURiqLQTebIZ+sxn6PqbETADMXO3MVdXcmrkZiCmmxMxmBnMHqbk2XWspSyllXct6PB7vn54e7u9f3d599PL2g299dH9/fzic7j71D/Fa+sd/JsSQYkwpsQgAbe20LK1WNXN3XNAZKIiA6czdTbXWui5rqbU0VTcAwhySSBAidjdtegYgiKSc5QKAqolwzpmFYXjGgMHcWmu1NQAxhBRT7hKLANDSjodjXdf2TM0VhjOH44JAYDBJCMIiTGTuAIIIMRMRAD+zC3c3dzczbaqmCoBFQExMAAjgM2IS4b/wNbz24uv/RgwxBAkh5b7fDMM4zfM8zfOu3wxdziBWNVNVM/r/yIK/X1vXqz7s3zHGM57n/THnXL/23rZ7AqkKtolrArgHFDWqmiIIx7SHpI1I/4tWiArlvneVehEq0kbuRS970cohjqJGRJGgCYVwkmDZxjj8ko99cs7ea6/fa875vu/zjDE61zzaDoLPh0iT5pJLLqpJCBHezNwdgJtHeK11P02ttXAHkabEzACIOaVUci5dJ8y1td12e3t0fX291FZyXq1Wp6cnIrLb7/e7/X7aL8viZiDSpCwiRI4wM2YpJR90pYiImUlK4zhqUgCadTWMpSssT5jZ3euyuEfSlHPuuw6QuiwAlZxz16nmgE/zXJcFcBIuOWvOKeVx1Z+cnGw2m/VmXUomgBkggPA9b737IY7e++TbL770AY7oF975wjRN17d3j/vdPNdvfP5bOPrEe38RIuAEkDuEpe/6oRtX4+rk5OzZ2enF+fn52cXpZtMPQ+lKSolTSqqsIipgYmIPQzMAzGAWYWLiwAEBIDAJJZGUVCSxSBIhEuEDqa3utrvdbjtN8+3tzcuXL6+uru8f7jXps+fPN5uTvitJRShUuXRdV3IumSUBzIQnRABbeG2t1jotLQBNBwIwWJIIQOHR3P7W//pXcPR3f+7/vru7u765uby6uXz9+vXlzc3d3Z983/+DI/3tHxFmSUlTUtWUkohERK11mvatGo483D0AMNMTfhIRblZbW+Z5XuqyVAsHwMSpiLCwSCDczN0BMLNISiKchIk8IonknInJzT0CQBy4u7mFC3HKepA1E1O41dqm7b4ui9XW3MI8EAAinIgJBEaSg5QkiSYAbgaAQCQMIA78KCLc48DD3N2eAGARErzB/U/9Md7Q997ORXPuci6aVJJmzd3QD/1qtV6tV6txsxmHQVWZuZmb+QEzl5xL9yQl4QgPjwgARATAm9VW9/t9rdUjkqSu70opKSVhBsDMquqBado/Pj7e3t7e3d5e39zUWkvOwzCu1isA28fHaT9Va+HuEUwkByzETIQIsEjOuWQtpYhIeEgSzVlVmViz9qXjJO7eaquthrtH4EhESikpJQSrJC255FL6DoR5XprViBCRUnI56Lr1enV+fr45OVkNgxYlgBkggPBnvPXuhzh6/yufAvBLX3x7mqZ/sPo1HP3g7/7A3NoSDmKIgBNA7hCWvuuHblyNq5OTs2dnpxfn5+dnF6ebTT8MpSspJU4pqbKKqICJiT0MzQAwg1mEiYkDBwSAwCSURFJSkcQiSYRIhA+ktrrb7na77TTNt7c3L1++vLq6vn+416TPnj/fbE76riQVoVDl0nVdyblklgQwE54QAWzhtbVa67S0ADQdCMBgSSIAhUdzq7Uuy1KX+rjb3t3dXd/cXF7dXL5+/fry5ububrvdvfqh38Ab47/5cU1JVVNKIhIRtdZp2rdqOPJw9wDATE/4SUS4WW1tmed5qctSLRwAE6ciwsIigXAzdwfAzCIpiXASJvKIJJJzJiY39wgAceDu5hYuxCnrQdZMTOFWa5u2+7osVltzC/NAAIhwIiYQGEkOUpIkmgC4GQACkTCAOPCjiHCPAw9zd3sCgEVI8AYTkzCLJP7Pvok3PvEn72hSSZo1d0M/9KvVerVercbNZhwGVWXmZm7mB8xcci7dk5SEIzw8IgAQEQBvVlvd7/e1Vo9Ikrq+K6WklIQZADOrqgemaf/4+Hh7e3t3e3t9c1NrLTkPw7harwBsHx+n/VSthbtHMJEcsBAzESLAIjnnkrWUIiLhIUk0Z1VlYs3al46TuHurrbYa7h6BIxEppaSUEKyStOSSS+k7EOZ5aVYjQkRKyeWg69br1fn5+ebkZDUMWpQAZoAAwve89e6HOHrvk2+/+NIHOKJfeOcL8zxd3dw97nfzXL/x+W/h6BP/+j8EC0gAauaJU5e7YRw34+ZkfXp+fnpxfv7s/OJ0czKuxq7vk6okZZEQBNwi3JubWasUSIlVJIkQMYgD8HCAhcAsqllSSiKqmjRrypLEmu12+4/d3d9dXV1dvn59+eoVgOfPX5yeng7j0BfVRJpEVVieMDOBiUiSMAuJhKNam+oyT7NZpKwpJWJh0ZwSsYDgFn/zV34cR7/y3/zq7e3d1c315eXry9fXl1fXt3f3f/Qf/GMcdb/zYyyShEVUNakqEUVEM1vmycwIHHDzCPNAHNABMxEBCPNqbVmWVpelWmvmCCaSLMLCIgA8DI5AEIiEkwiziDCBWEQ1AWhm4R4RAMIjEB6RRFQ1JT0A4Ga11mWa6lKttubmZhEOgIgJRMLCJJLkICVNKSJabYEgEJhw4OHuFh4eQDzxMHcPc7MAiBh/yvgz38Yb6Xe+kA+05NypZlHJueuHfuhXq9Uwrg7W3TCoKhG7eTMLd2IupfRd1/eDamIKRAAgIpYEwM1qq/v9vi4VgGYdx7Hr+lKyiETggJlabbvd9v7+/ubo9vZ2qS1rKl23GseIuL9/mOcJbxAfiBCxMLEIkyTNmnLOmnNKiZmJCAAza0pJNWsGsNRlnqbtbmdmIoKIWisAVWVJDFbVvu+Hg/Vacw43AMRIIppzKbmUslpvLi7ONycnq2HQogQwAwQQ/oy33v0QR+9/5VMAfumLb8/z9OXx13D0g1/9gcVs8QgmsIAEoGaeOHW5G8ZxM25O1qfn56cX5+fPzi9ONyfjauz6PqlKUhYJQcAtwr25mbVKgZRYRZIIEYM4AA8HWAjMopolpSSiqkmzpixJrNlut//Y3f3d1dXV5evXl69eAXj+/MXp6ekwDn1RTaRJVIXlCTMTmIgkCbOQSDiqtaku8zSbRcqaUiIWFs0pEQsIbrG0Wmurddlut7e3d1c315eXry9fX19eXd/e3T8+bF9+9tfxxuarPyGiqklViSgimtkyT2ZG4ICbR5gH4oAOmIkIQJhXa8uytLos1VozRzCRZBEWFgHgYXAEgkAknESYRYQJxCKqCUAzC/eIABAegfCIJKKqKekBADertS7TVJdqtTU3N4twAERMIBIWJpEkBylpShHRagsEgcCEAw93t/DwAOKJh7l7mJsFQMT4U1iIWJKk9J9/C2+8+MOfUc2iknPXD/3Qr1arYVwdrLthUFUidvNmFu7EXErpu67vB9XEFIgAQEQsCYCb1Vb3+31dKgDNOo5j1/WlZBGJwAEztdp2u+39/f3N0e3t7VJb1lS6bjWOEXF//zDPE94gPhAhYmFiESZJmjXlnDXnlBIzExEAZtaUkmrWDGCpyzxN293OzEQEEbVWAKrKkhisqn3fDwfrteYcbgCIkUQ051JyKWW13lxcnG9OTlbDoEUJYAYIIHzPW+9+iKP3Pvn2iy99gCP6xXe+sJ+n67u7x+1uru3rn/t9HH3qd/+jIApIBGozhvRdGbrVen1yujk5Pz09Ozt/dn5+fnq62qzHcSxdn1TAYmFzW5Y6T/PUliXMCMiackpZE3ECKBB+EAFAiJlT0qRJS+lK33ddUc3mMe33037a7naP2+3Dw+Ory1ff/vb7VtuzF8/PT89OTsa+77qckhAQQCACRMScUlLNSTSpBtDM5laXefGApJxUSSRJSkmJBYC7/Y3/5cdx9Pd+/h/d3NxeXV9fvrq6vLx8dXV9e3v/x3/hH+Mo//aPJk1JWERSUhZhImY2d2vN3HHkZu7WzOERCAKBCR6BaGZWWzOrVs2sVgMjJWEWFmEmAO7hYQCYRISZJYmwMDEnkYiotfpBBIDwAEBMSSTnrJpEBECtZq3WuVqtdanVanhEOBETE5OkJEQswk9EhNk9Wq2BYGYCAQhEeATCI8KDmMIjwt2tmYWHwwF44CA8Tn72u3iDf/tHS8k5dzmXpEUl5VK6YRiHoR/G1cF61feDqoK4NTtwMyYuXSldNwxDKVlZWBjhINYkzOLhtbZ5v6/WCKRZx2Ho+r4rRVWJBUC47ffT/cPD3e3N1fX1zfX17e3tvCyatJQ8jqO5P9zfz8tCxEQQFmYCMRGSCIuo5qwpqeakmvVjHjFNEyL0YzmH+3a32223D4+P1oxFwm2335sZE3mgVcuqq9Xq7Pzs4vnzzWZTur6UrCpJkqpKEhYZxuH87OzkZLNar0vJBDADBBD+jLfe/RBH73/lUwD+zhff3s/Tl8dfw9Gnv/rp2c3CnTmIAhKB2owhfVeGbrVen5xuTs5PT8/Ozp+dn5+fnq4263EcS9cnFbBY2NyWpc7TPLVlCTMCsqacUtZEnAAKhB9EABBi5pQ0adJSutL3XVdUs3lM+/20n7a73eN2+/Dw+Ory1be//b7V9uzF84ZWwxQAACAASURBVPPTs5OTse+7LqckBAQQiAARMaeUVHMSTaoBNLO51WVePCApJ1USSZJSUmIB4G6tWa11Wdrjfntzc3t1fX356ury8vLV1fXt7f3j9vHVZ38Db2y++hMikpKyCBMxs7lba+aOIzdzt2YOj0AQCEzwCEQzs9qaWbVqZrUaGCkJs7AIMwFwDw8DwCQizCxJhIWJOYlERK3VDyIAhAcAYkoiOWfVJCIAajVrtc7Vaq1LrVbDI8KJmJiYJCUhYhF+IiLM7tFqDQQzEwhAIMIjEB4RHsQUHhHubs0sPBwOwAMH4SFCzMIi3U/+Ed54/oc/nbSopFxKNwzjMPTDuDpYr/p+UFUQt2YHbsbEpSul64ZhKCUrCwsjHMSahFk8vNY27/fVGoE06zgMXd93pagqsQAIt/1+un94uLu9ubq+vrm+vr29nZdFk5aSx3E094f7+3lZiJgIwsJMICZCEmER1Zw1JdWcVLN+zCOmaUKEfizncN/udrvt9uHx0ZqxSLjt9nszYyIPtGpZdbVanZ2fXTx/vtlsSteXklUlSVJVScIiwzicn52dnGxW63UpmQBmgADC97z17oc4eu+Tb7/40gc4ol985wv7ebq+u3vc7ebFvv65b+Lora/+gAMOaRZWK0FyKWM3rMfNyWZzdnp2cX7+/OLi9ORsc7IZVmPfdZoVzM19sWVapmnet1rZnZmKalbNmkUEIAu31ppZuANEJEkOUncwDF3f55wBTNOy38+73Xa/30/z8urVq2/92z+Yp/3zFy8uzs9OT082Y9flxIRm1a2ZOYCkKanm3KnmlBTM7l7drBmYNZWUlUhIJEkC2OFm9nO//DaO/t7P/6Obm7urq+vLV68/ury8fH15fXP//l/8JzhKv/WXNSXiJ0mERZII4YlHuDuAANysmbl7uEeEezATjtzDw8MPolqdp9ngIsJJkiQmBhCIA/cAwEwikiSJJmEGYO6tVjMHAv8eqaacs0hSlQjU1rw2a+a1LXWptblZIIgpyYEmEWYGEwA6AHm4NwsEMxMIQCDCIxB4wyPCw82aW5g7IuAeCA8AJz/7XbzBv/2jpeScu5xL0qKSciml74ZuHFbDuFqt15uu70vOIG7NrLbmBqDkXLpuHIZSSpeVRdyMmVSVWUBw97osZkZEKaWu70sumlU1CxOBPWyapvv7h5vbm+vr65ubm+vr63lehClr7sfRze7u7+q8SBJiSSLMhAPiJKKauq5T1aSqKbFIybl0HSL2+31tTURUtZRiZo8PD9vtdrfb1daEpZlN+30zAxBudTbRtFlvzs5On794cXJ+No6rvu9LTpo1iZAIi4xDf3p2drrZrFajlkwAM0AA4c94690PcfT+Vz4F4O988e39PH15/DUcfeZrn6luLcKJHHBIs7BaCZJLGbthPW5ONpuz07OL8/PnFxenJ2ebk82wGvuu06xgbu6LLdMyTfO+1cruzFRUs2rWLCIAWbi11szCHSAiSXKQuoNh6Po+5wxgmpb9ft7ttvv9fpqXV69efevf/sE87Z+/eHFxfnZ6erIZuy4nJjSrbs3MASRNSTXnTjWnpGB29+pmzcCsqaSsREIiSRLADjezWlutdVna4357c3N3dXV9+er1R5eXl68vr2/uHx4ebz7/L/DG8K/fZuYkwiJJhPDEI9wdQABu1szcPdwjwj2YCUfu4eHhB1GtztNscBHhJEkSEwMIxIF7AGAmEUmSRJMwAzD3VquZA4F/j1RTzlkkqUoEamtemzXz2pa61NrcLBDElORAkwgzgwkAHYA83JsFgpkJBCAQ4REIvOER4eFmzS3MHRFwD4QHAGISYtHU/eQf4Y3nf/jTSYtKyqWUvhu6cVgN42q1Xm+6vi85g7g1s9qaG4CSc+m6cRhKKV1WFnEzZlJVZgHB3euymBkRpZS6vi+5aFbVLEwE9rBpmu7vH25ub66vr29ubq6vr+d5EaasuR9HN7u7v6vzIkmIJYkwEw6Ik4hq6rpOVZOqpsQiJefSdYjY7/e1NRFR1VKKmT0+PGy3291uV1sTlmY27ffNDEC41dlE02a9OTs7ff7ixcn52Tiu+r4vOWnWJEIiLDIO/enZ2elms1qNWjIBzAABhO95690PcfTeJ99+8aUPcES/8M4X5nm6urnbTvul2dd+6Pdw9H1f+7Q7WqA2X5bGoJJzV7quG0/XJxdn5xfPLj7x7PnZ6elqs+6HoeSsWSECJoebt9oqwhMhi2jWLueshZgDaM3qMi91qbWZO8BCzCKquZQ+l1z6noF5qfM0P26387K4+0cvX/3eN76x2+2ev3jx7NnFxdnpetXnxAyv81StmhmBUlbVnEufNGtKLAxigJw5pVRKr6ogAhgkHl5bq9V+7pe/gKNf+Vv/8Pbm7ur6+uWr1x+9evXRq8vr69sPf/Cf4Sj+37+kkkiYQCJMzElYRIgFQHhEuLuZR5g3Mw+HBw6YnoCY6QAgMGqtj9ut1UYilEiTMjNAAMLdw92DmUREj4jI3N1sWWq44w1iAiiJ5JJT0pQkPKrVMKeAN1vmpbbamgUisbBIVk2aiAiAmQcizAMRHgCYGUz4mEcgwgNHgbAnrZmFgxgBhEfAPbB55zt4I/3OF3LOpZRc+qyFRVRzybkbhnEcV6v1ZrMZxlFzZmZrVmtbakVEklS60nV935e+64S5WQOQJCVNmhQEN/PAQRLRlJIqEfETAjGAVutuv3t4eLi7vb25ubm6vt7v9gAkSd/1zezh/m6elyQiSVQzCAgwk7Dkkodh0JwTi2gSkVLKOAxEvCxza80jUkp937vZ7d3dbrdze+IRblabIRzEOHCISOm61Wp1en62Wq/7rsuldEVVs7BIlqR5s15dXFxs1uvclZSEAGaAAMKf8da7H+Lo/a98CsAvffHteZ6+PP4ajj779c9Waw0IZne0QG2+LI1BJeeudF03nq5PLs7OL55dfOLZ87PT09Vm3Q9DyVmzQgRMDjdvtVWEJ0IW0axdzlkLMQfQmtVlXupSazN3gIWYRVRzKX0uufQ9A/NS52l+3G7nZXH3j16++r1vfGO32z1/8eLZs4uLs9P1qs+JGV7nqVo1MwKlrKo5lz5p1pRYGMQAOXNKqZReVUEEMEg8vLZWq83LUV12u93tzd3V9fXLV68/evXqo1eX19e3D4+Pjz/6L/GG/vaPEEiEiTkJiwixAAiPCHc38wjzZubh8MAB0xMQMx0ABEat9XG7tdpIhBJpUmYGCEC4e7h7MJOI6BERmbubLUsNd7xBTAAlkVxySpqShEe1GuYU8GbLvNRWW7NAJBYWyapJExEBMPNAhHkgwgMAM4MJH/MIRHjgKBD2pDWzcBAjgPAIuAeYICKctPy1P8Abn/iTd7IWFlHNJeduGMZxXK3Wm81mGEfNmZmtWa1tqRURSVLpStf1fV/6rhPmZg1AkpQ0aVIQ3MwDB0lEU0qqRMRPCMQAWq27/e7h4eHu9vbm5ubq+nq/2wOQJH3XN7OH+7t5XpKIJFHNICDATMKSSx6GQXNOLKJJREop4zAQ8bLMrTWPSCn1fe9mt3d3u93O7YlHuFlthnAQ48AhIqXrVqvV6fnZar3uuy6X0hVVzcIiWZLmzXp1cXGxWa9zV1ISApgBAgjf89a7H+LovU++/eJLH+CIfuFnfmyap5v7+91uPzX72g/9Ho6+/+ufsUBtVpst1cJRkubcDd2wWW+eXVw8v7h4fvHs5OR0XI1d15eulJIl55SVEhMjEEKhRMqSS845Ze2IKCJarftlXuZlXua6tAg8IU4pac6aO9VExNZsnpbtbrssiwOvL1///u9/a7t9PD8/v7g4v7g424x9YsDbsky1VjeDcMkHJZdeNXNKqkmSppRZRXMpuRMRAyJghmpW52Velr/5Kz+Oo1/+r3/17vb26urm8vL1Ry8v/91HH11d37z6od/Akf/6Z0WEmQkEpgNhZmISJqI4MPdwcw/zAwvHkRAfkLAwMzGEDmpr28fH2iqxsEgS4SRMBMDMgXB3AhGzapKUmKg1c7Nmzd0jAgARMbMcqSqLCJM73AwAAVFtWZZqzc3Cg0WSiGgSZhyZe5ibGwBmJhAOmPA9HgACER6BsCetmeGAGE/cLQK++pnv4A197+3SlYOcO9WsSZNqEi2lG1bDarVarzfDMJausIhbtNZqre7OIqqaD1SzJk1CxEmEkwhLSgIiRPhRRAAg4qRJmEEkIqoKoNa6225vbm9vrq6vrq+2ux0CzKQ5e7OHh4faqogwsSQBEQBmzjn3Xd8PfSlFmFNKLJJzHoeBRVqt5k9EpJRiZo8PD/OyMLO713nxcBAxiJiYWURTUk2plNKNQ9d1mg80H2hKoinnrsvrzfri4mK9XuWs/AQgPCH8GW+9+yGO3v/KpwD80jv/yTRP/2D1T3H0l77+2SW8AUFkgdqsNluqhaMkzbkbumGz3jy7uHh+cfH84tnJyem4GruuL10pJUvOKSslJkYghEKJlCWXnHPK2hFRRLRa98u8zMu8zHVpEXhCnFLSnDV3qomIrdk8LdvddlkWB15fvv793//Wdvt4fn5+cXF+cXG2GfvEgLdlmWqtbgbhkg9KLr1q5pRUkyRNKbOK5lJyJyIGRMAM1azOy7wsc11qrUttj9vt3e3t1dXN5eXrj15e/ruPPrq6vnl83O7e/ld4Q37z8wQC04EwMzEJE1EcmHu4uYf5gYXjSIgPSFiYmRhCB7W17eNjbZVYWCSJcBImAmDmQLg7gYhZNUlKTNSauVmz5u4RAYCImFmOVJVFhMkdbgaAgKi2LEu15mbhwSJJRDQJM47MPczNDQAzEwgHTPgeDwCBCI9A2JPWzHBAjCfuFgEHwCyiWv7aH+CNT337Z1WzJk2qSbSUblgNq9Vqvd4Mw1i6wiJu0Vqrtbo7i6hqPlDNmjQJEScRTiIsKQmIEOFHEQGAiJMmYQaRiKgqgFrrbru9ub29ubq+ur7a7nYIMJPm7M0eHh5qqyLCxJIERACYOefcd30/9KUUYU4psUjOeRwGFmm1mj8RkVKKmT0+PMzLwszuXufFw0HEIGJiZhFNSTWlUko3Dl3XaT7QfKApiaacuy6vN+uLi4v1epWz8hOA8ITwPW+9+yGO3vvk2y++9AGO6L/7qR+el+V++7jbT/upfu1z38TR93/9s2Y+mbdqtTYEmKhoP46rk/XpxcXZ8/Pz87Pz9Xoz9H3pumHo+6EfVqsyjl2fkyZmECEBSViTqKaUlEjCvbU2L8s8T/tprstSmzXz8GARzUVFATiAwLIsu91uWRbzuL27e//97+x222EYT0/Wz5+dD0PHYW611TmsOcAiJedSOs2d5qIHpXSl5K4rXS+ahQSAAWY2V1uWZT9N89J+/n/7Kzj6u3/jyze3t1dXN1dX1x+9fPn+Bx9cX91df/5f4Mh//YdEmEDERCAAgQAQHjgKhJkD4eYBdwczCMzCIsLMxMzELESgam2/37dmrHLALCJMIBwFAkcEIia84e5mHu7NLBDCLJJyzqqJmPAxBxgEwOCt1aWam0cASCIECkR44CgQHuFmRJxVSTgi4IGPMQEgojhwb2bhbtbcEQiAwAiPCG9mm3e+gzf63/2JkrvSlZw71ay5JEkkklMufRmGcVythmEoXaeqCHL32pq7MxGLaEqImJepqJ6enQ3DkEQAtNocAaAudbfbzcvcamWRYRhyzsKsOY/jmHNm4t1ue3V0+fr1brcDwESS1M12u22rjUUANDMAwpRzHsZxHIa+7zVnEUkikpKq9l0HkFkDIJKYCUAzq8sCIKm6+3a7jYiu65JIM0tJV8OouWMmZgaLCElSVckHmlQ159z1/bhenZ2ejqux73JKQoSDIPx5b737IY7e/8qnAPziT//IvCz/cPPPcPTZr3/GAAMMZOaTeatWa0OAiYr247g6WZ9eXJw9Pz8/PztfrzdD35euG4a+H/phtSrj2PU5aWIGERKQhDWJakpJiSTcW2vzsszztJ/muiy1WTMPDxbRXFQUgAMILMuy2+2WZTGP27u799//zm63HYbx9GT9/Nn5MHQc5lZbncOaAyxSci6l09xpLnpQSldK7rrS9aJZSAAYYGZztWVZ9tM0L81ara0ty/K43d3c3l5d3VxdXX/08uX7H3xwfXU3L/P2C+/hDf7n/zExEQhAIACEB44CYeZAuHnA3cEMArOwiDAzMTMxCxGoWtvv960ZqxwwiwgTCEeBwBGBiAlvuLuZh3szC4Qwi6Scs2oiJnzMAQYBMHhrdanm5hEAkgiBAhEeOAqER7gZEWdVEo4IeOBjTACIKA7cm1m4mzV3BAIgMMIjwptZeIhQ0tz95B/hje/77ruqWXNJkkgkp1z6MgzjuFoNw1C6TlUR5O61NXdnIhbRlBAxL1NRPT07G4YhiQBotTkCQF3qbrebl7nVyiLDMOSchVlzHscx58zEu9326ujy9evdbgeAiSSpm+1221YbiwBoZgCEKec8jOM4DH3fa84ikkQkJVXtuw4gswZAJDETgGZWlwVAUnX37XYbEV3XJZFmlpKuhlFzx0zMDBYRkqSqkg80qWrOuev7cb06Oz0dV2Pf5ZSECAdB+NPeevdDHL33ybdffOkDHNF//9M/Ms/Lw+5xu5+2+/nrn/smjr7/65+pFnO11qw1B8DgomUcVpv1+vzs7OLs7Oz0fLNa9cPQD/0wDKv1arU5GdarftWXoiwiDAaEISJJJIkQCQBrNtelLst+2i/zMi3WWm3WGCxJmdncwhDAsiyP2+08LR7+8PD48uXLab/XnDfr1cWz86FktwVW3RsDzKJZS9+VXDR3mnNKqqX0XZ/7vu861uwBBDy8WUzzMs3LtNvPbfnbf/+v4uh//i//r5vbm6urm6urq49evvruBx/e3N7d/PD/hyP6558jEDERCEAgwqOZAfHEI+DuCESE44iICSTMLEzMQnxAwmBys3mem5mIsAgnEWIAxAQgPHBETOGBNwLhEd6sWgMgzEm1lJKS4Cg8ALAIBcLcW6tLNTcABGJmANVauEcEjsKjuSWW0hUWCfMDAMREIDAdRIQ3s3A3czd3HATjY+FWm23e+Q7e6H/3J7quz6XLmnPusmbRRMwiqeSuG/pxGIZx7Ptec2YSjzAzdwfAzJqSu9dlKqU8f/ZsGEZmcvdpnt0MRHVZHh4fp/1+miZiXq1WJWdzzzmv1+uh71V1npeb25ubq+ur66vtbhfuAIi4tjrt981MiB1odQEgLKUrq9VqHMeu70vOIpJSkpRUtZTCRLVWAKqZmWprbubuLNKVLhD73Q6gYRiSJjNLKY39qJpBjHCLAIFYUpKSs6poyqXP/TCs1pvTk80wDkOfUxIcBeHPe+vdD3H0/lc+BeB/+Os/Ns/Lr27+KY4+87XPOMEAA6rFXK01a80BMLhoGYfVZr0+Pzu7ODs7Oz3frFb9MPRDPwzDar1abU6G9apf9aUoiwiDAWGISBJJIkQCwJrNdanLsp/2y7xMi7VWmzUGS1JmNrcwBLAsy+N2O0+Lhz88PL58+XLa7zXnzXp18ex8KNltgVX3xgCzaNbSdyUXzZ3mnJJqKX3X577vu441ewABD28W07xM8zLt9nNbWrNW67QsDw/bm9ubq6ubq6urj16++u4HH97c3i21br/wHt6Q3/w8gQAEIjyaGRBPPALujkBEOI6ImEDCzMLELMQHJAwmN5vnuZmJCItwEiEGQEwAwgNHxBQeeCMQHuHNqjUAwpxUSykpCY7CAwCLUCDMvbW6VHMDQCBmBlCthXtE4Cg8mltiKV1hkTA/AEBMBALTQUR4Mwt3M3dzx0EwPhZutVl4AMi59D/1R3jj+777bs5d1iyaiFkkldx1Qz8OwzCOfd9rzkziEWbm7gCYWVNy97pMpZTnz54Nw8hM7j7Ns5uBqC7Lw+PjtN9P00TMq9Wq5GzuOef1ej30varO83Jze3NzdX11fbXd7cIdABHXVqf9vpkJsQOtLgCEpXRltVqN49j1fclZRFJKkpKqllKYqNYKQDUzU23NzdydRbrSBWK/2wE0DEPSZGYppbEfVTOIEW4RIBBLSlJyVhVNufS5H4bVenN6shnGYehzSoKjIPxpb737IY7e++TbL770AY7oF975wrws94/3j7tpu5u+/rlv4uj7vvbp6r5UM4NZMEhEsuaSh/W4Oj3ZnG7Ozs9OTzebYbVarVercbXarDcnJ/1q6Mc+F2URZiQiYk4MYTkAE4M9YK0urS3LMs/LPC21Lq1V82CIw92jmXn1aZkfHx6meXaLx+328vL1PE+qulqtnl+clpzqvHdvSThn7UvXDX3X9yUX1ZxS5iRJs5ZcSpdzIRG3MDxp5nVp07zM835a6t/++/8pjv6nd/7Pq6ury9fXr19fvXx1+eHLV/d3D7c/8ls4kt/8PIHAdAAgDtybmZuFh7l7GBxPGAdEjCMCMYNZiFiED0g4IpZ5cTgxEzExizCBcBQIHBGImAAQURx4WLg3a2YARFgPck4sYMARCADERAAM3tqyLM0MABMxc3g0a2bNHR/zMDdjka7rk4gfRIQHMQkxMREoEM0s3N3c3AOBJwQGAe5hVlc/8x280f2bn+hKl3NRLSnnrDnnRKIppZJz6bphXPXDsBpHzYVZAFhrHsFEIpJUNYkI91232WxU1d2ttWmezYxB1dp+t9tP0zLNALqhBzBPEzP3/TAMfek6ALvd7uHh4e729nG7XZbFmjWztiz7eXIzFoGHuXlARTTncRz6YRz6LpeSUtKjkrPmDKAuFYiUEohabR4OIIl0Xc/C1hoR932fNBExE5gEJEQ4sAAIzKRJNYtqTkn6vh/GYbPZnJycrFZDKZqS4CgIf95b736Io/e/8ikAv/TFt+dl+fLwT3D06a9+2uANCObqvlQzg1kwSESy5pKH9bg6Pdmcbs7Oz05PN5thtVqtV6txtdqsNycn/Wroxz4XZRFmJCJiTgxhOQATgz1grS6tLcsyz8s8LbUurVXzYIjD3aOZefVpmR8fHqZ5dovH7fby8vU8T6q6Wq2eX5yWnOq8d29JOGftS9cNfdf3JRfVnFLmJEmzllxKl3MhEbcwPGnmdWnTvMzzflpqa21Zlmma7h8er66uLl9fv3599fLV5YcvX93fPSy17t7+V3gj/dZfJiIAceDezNwsPMzdw+B4wjggYhwRiBnMQsQifEDCEbHMi8OJmYiJWYQJhKNA4IhAxASAiOLAw8K9WTMDIMJ6kHNiAQOOQAAgJgJg8NaWZWlmAJiImcOjWTNr7viYh7kZi3Rdn0T8ICI8iEmIiYlAgWhm4e7m5h4IPCEwCHAPs2oWxKRJ+5/6Y7zx1rf/q5Rz1pxzItGUUsm5dN0wrvphWI2j5sIsAKw1j2AiEUmqmkSE+67bbDaq6u7W2jTPZsagam2/2+2naZlmAN3QA5iniZn7fhiGvnQdgN1u9/DwcHd7+7jdLstizZpZW5b9PLkZi8DD3DygIprzOA79MA59l0tJKelRyVlzBlCXCkRKCUStNg8HkES6rmdha42I+75PmoiYCUwCEiIcWAAEZtKkmkU1pyR93w/jsNlsTk5OVquhFE1JcBSEP+2tdz/E0XuffPvFlz7AEf3iO1+Yan14fLjf7ra76euf+yaO/sJXf6AGajM3BEBgkaRJc+rGrt9sNiebzfnp6enJ2cnJyeZkvV5v1pv1uDkZV30Zei1JhFkkiSSAGMwkfCDMAlBEmFldlqUu07RM87LUxZq5eTNzi2Z1Wep2t7+/v5+nqZnvdvub6+t5XjSn1TienZ5k5Xm/I4qxK8MwbjbrcRz7ccilJFERAYloSqrpQNVB1swiAmQetVqdl3mZ5rn9t//7X8XR//hf/B+Xl69fvb66fPX69dXV66ubh8ft/Y/+Sxyl3/xhMDExMwEwdzdrZm72/5MFr6+2r9d92L9jjOfyu83LWnOtfc7xTmyCI8lyrNqSjy3FNzXBRolhg2lp/Sbv/KIOtIZS2rShtH9CKS1uHeVF/oIWKkhaDIWUYjfRcWTFx4ocMNE50dHeZ6/7dc7f8zxjjM41j3c4oM/HTNXcVN2NiAkEBhHjwN0AELEwiQQSDiwONFN3c9ATZmJiIgI53NzdHIAIE4iFAQLc1NTNmpobACaOMYYUAwsOHA6AQNgzt9bKXJoqAGJiInPX2pqpu8Gw5/DWVES6LrOIm7k7ANpjZiIcqKqpqZnD8e8wEWDmqnX62r/FG923fj7nlFIXYooxpZRjjBJiCCFKzH3uh3Ecp3EcctcFCeYwVewRhRBySjmnoe/6rks5M7Oq1lpbrU2ViKzpXOZ6ACCmZGqP20e4x5TyAYFqq4+Pj7c3N/f399vH7a7MrWktpZaipsQCQFUBiEhOKXd56Pqu7/ZiSjmlmFJOKabk7qUUNxcRAObm5gBEJOccYiAiZo4hsAQRYSKAmQjERHACiIRYoqQYJMQQJOd+WgzTYnm0Xk3TmHMMgXHghB/2/MVLHHz4jXcA/Nd/+91drf9b/3/i4DPf/vEGKGBAddSmpnCAwCIhhphCN3b9crlcLZfH6/V6dbRarZarxWKxXCwX43I1Tn0e+piDCLNIEAkAMZhJeE+YBSB3V9VaSqlltyu7uZRatKmpNVVTb1pLqQ+P29vb23m3a2qPj9ury8t5LjGFaRyP1qsUed4+EvnY5WEYl8vFOI79OKScg0QRAYnEEGIMezEaSJuqu4PUvFatc5nLbp5bbWXezQ+77c3NzdnZ+evzi7PX5+cXF+cXV3f3D7W17bv/Am+kf/YzzARAzUy1qZqqmaq5qbobERMIDCLGgbsBIGJhEgkkHFgcaKbu5qAnzMTERARyuLm7OQARJhALAwS4qambNTU3AEwcYwwpBhYcOBwAgbBnbq2VuTRVAMTEROautTVTd4Nhz+GtqYh0XWYRN3N3ALTHzEQ4UFVTUzOH499hIsDMPxN3TQAAIABJREFUVauqE1MMqf/VP8cb73zw6zGmlHKMUUIMIUSJuc/9MI7jNI5D7rogwRymij2iEEJOKec09F3fdSlnZlbVWmurtakSkTWdy1wPAMSUTO1x+wj3mFI+IFBt9fHx8fbm5v7+fvu43ZW5Na2l1FLUlFgAqCoAEckp5S4PXd/13V5MKacUU8opxZTcvZTi5iICwNzcHICI5JxDDETEzDEEliAiTAQwE4GYCE4AkRBLlBSDhBiC5NxPi2FaLI/Wq2kac44hMA6c8GnPX7zEwXtvv/vs6x/hgP7zv/WluZTb+9v7x+3D4/z+T/4rHPylb/94ddTmpuYAQ4KEEGIMqcv91A/r5WK9Pj4+Wq+Pj47WR+v1arFcjoupH4fUdzEFFpYYcozMxO4gMIkwsURhAjPcWtNSyrybt3P5RJ1rac1MS6mP2+393f3V9dV2uzW17W6+ublptQRJw9CvlwsRrvNDEF6uFuv1+vjoaLlcDeMQUycsxAAEDBIRFmJpZmWemypI3K1VraXtatn7O//ob+Dg73/5d1+/fv3q9dnZ+cXFxfXN7d3Dbvf4pT/CQfiDL5CwMBMRgKZqqrU2U1Uzc9Wq5hABk4BBxO4GwM3djYgJxCIhCosws5k73OEAERNAQQSAw83dzfCERFgkEJObm6q6WVNzIyJmiTFICMJMIADmBoD2HN6saWu1qiqeEDG5uWptqm6OA3NTdSGKKQmzw8ywxwxmISKAADc1NXO4u+ETxMQEwM1NdfzaB3ij/+Ofz6lLKUuMKXXxIMQkIsycc+6HcRinaRqHvmeJAEwVADOHGLucc5envs85sQgA0yetqbuZH6gZfE+IQwzmPu92tTUAwswiAFpru+32+ubm/u7u/v5+u5trKaZPzM3dAZiDCUQkIXYp5S53e33fd13uuryXUogR7vM8mxmz4C84QCIcQhAJIgJAVZkphhhiFIkxRJY9AjGLsOxRkBijEFOXun7qF4vFer0ep7HvYgiCAyf8sOcvXuLgw2+8A+C/+tvvzqX878P/hYPPfPszCmuAAdVRm5uaAwwJEkKIMaQu91M/rJeL9fr4+Gi9Pj46Wh+t16vFcjkupn4cUt/FFFhYYsgxMhO7g8AkwsQShQnMcGtNSynzbt7O5RN1rqU1My2lPm6393f3V9dX2+3W1La7+ebmptUSJA1Dv14uRLjOD0F4uVqs1+vjo6PlcjWMQ0ydsBADEDBIRFiIpZmVeW6qIHG3VrWWtqtlr9a63W7vHx+vr65ev3796vXZ2fnFxcX1ze3dw25nVXdf/mO80X3zi0QEoKmaaq3NVNXMXLWqOUTAJGAQsbsBcHN3I2ICsUiIwiLMbOYOdzhAxARQEAHgcHN3MzwhERYJxOTmpqpu1tTciIhZYgwSgjATCIC5AaA9hzdr2lqtqoonRExurlqbqpvjwNxUXYhiSsLsMDPsMYNZiAggwE1NzRzubvgEMTEBcHNTVTcmhJi6v/nneOOdD349pS4ehJhEhJlzzv0wDuM0TePQ9ywRgKkCYOYQY5dz7vLU9zknFgFg+qQ1dTfzAzWD7wlxiMHc592utgZAmFkEQGttt91e39zc393d399vd3MtxfSJubk7AHMwgYgkxC6l3OVur+/7rstdl/dSCjHCfZ5nM2MW/AUHSIRDCCJBRACoKjPFEEOMIjGGyLJHIGYRlj0KEmMUYupS10/9YrFYr9fjNPZdDEFw4IRPe/7iJQ7ee/vdZ1//CAf0O7/6hbmU+8fH+8ft425+/ye/i4O//CefqWa1uqo5QGCRECWmkHPKY9cvl4uj1XpzdHx8erLZHK/W68ViMUxj6vvUpZiDhBhi6HIKzHA8IQhLCLLHIgBMrbW225V5LnPZ7bbzvCu1lmY6z/P9/f3V9fXV5eXD46OqzvP8+PDQagscutyN0xAD11q6HDab481mc3Jyslqvx2GMMREBRAA7AAKIHNxUd/NO1ZmgDmtaq+5qqaX8nX/0N3Dw9979n159/PHr12cfn51dXd1e393vdvP23W/hIPzBF1j2mEAOb6qmWmszVTUzfWLuIsQkYPwFg7m6YY8YLMIiQQIJC7PBzRwAMRGzEBOTm6ubNXU4gUIMIUYiMjVTbapu5nACcZAgIiEwPQHg7viEualZbaVWUyWmPYAAr7WZqsNx4G5NIQSJQsTuBgMYRCxMzIIDM1VzV8MnGCAATExubqrj1z7AG8O3/3ruUkpdDCnupRRDCiGQCIAUYzdM4zgsl8su9zFFgMzM3UUkppRz7vtu6ocYBYA59tzUzFS11aZuAIhIRIKEEIRYAKhqrcWaAmi61+4fHq6vr+9ub2/v7raP26bqqgbATc3djYiZQEQsEkRSSrnr+r6fxnEYhtx1OaUQgrnXUpoqE4EI7gDMHQdBJKYE9+1u5+5dziGmyCGmFGMMMUoMMUSJIYQYo4QgQpK6NIzDtFiuV8txGvsuhiA4cMIPe/7iJQ4+/MY7AP6LX/vpuZT/Y/l/4+Az3/6MMRpgQDWr1VXNAQKLhCgxhZxTHrt+uVwcrdabo+Pj05PN5ni1Xi8Wi2EaU9+nLsUcJMQQQ5dTYIbjCUFYQpA9FgFgaq213a7Mc5nLbred512ptTTTeZ7v7++vrq+vLi8fHh9VdZ7nx4eHVlvg0OVunIYYuNbS5bDZHG82m5OTk9V6PQ5jjIkIIALYARBA5OCmupt3qs4EdVjTWnVXSy1Pttvt3f391dXVq48/fv367OOzs6ur2+u7+91ubmr1y9/GG/17XyKQw5uqqdbaTFXNTJ+YuwgxCRh/wWCubtgjBouwSJBAwsJscDMHQEzELMTE5ObqZk0dTqAQQ4iRiEzNVJuqmzmcQBwkiEgITE8AuDs+YW5qVlup1VSJaQ8gwGttpupwHLhbUwhBohCxu8EABhELE7PgwEzV3NXwCQYIABOTm5uqw5glphS/+q/xxl/6ty/iXkoxpBACiQBIMXbDNI7Dcrnsch9TBMjM3F1EYko5577vpn6IUQCYY89NzUxVW23qBoCIRCRICEGIBYCq1lqsKYCme+3+4eH6+vru9vb27m77uG2qrmoA3NTc3YiYCUTEIkEkpZS7ru/7aRyHYchdl1MKIZh7LaWpMhGI4A7A3HEQRGJKcN/udu7e5RxiihxiSjHGEKPEEEOUGEKIMUoIIiSpS8M4TIvlerUcp7HvYgiCAyd82vMXL3Hw3tvvPvv6Rzig3/6Vz5XS7raP291uN9c//ak/w8GPfecnmtquqlY1dxiJSJSYY5+7ru+61Tgdr482m+PNs9PNZnO0Xo/TlIc+d13MQXKKMeacUs5BBO7YIzDLXghBWEAMN1WdSym7Umqbd9vddltqNfPH7fbm+ury8vLs/Pzx4aG2VkvZ7nbWlMAppr7PMUUmTGN/ero5OT19dnq6WK26ro8xAIw9AkAOd4ca1FSbmhuIzayp1tpqqbWU3/yHv4yDv//l33318cevz87Ozi8vLq4ur28eH3a7L/8xDsIffIGEhZmJHd5UtbamaqoOa6qm6gZiEDExYc/gcFMzdwBMxMIkxCxBAouAycwAELMIiwgAd2+q2pqqibCEEENkYVMz1abNzAAwsxwQMxExEQAicndzdzVvqq3VUtWMGcxCRABqbeYKwxMGDAaHg4kAmCsAIiYmJhFmHKiZucLgcAJhTwAwMbm5qY5f+wBvLN//pdylFDuJKcUYUxIJwgJiABIk9/00Tovlsu+HFBMxqyoAEUkpDX2fuzzkHIKYGYiZCYCZlzLvtttaq5mzcE5JQhCRrutWyyWzbHfbWkqtdS5l3u1u7+6uLi9vbm5vb262887M4QbA3ZsqAGZhAhGBmJliiDmnoR+mxTSOYz8MKaUQAtxrrU0VgH/CrOmTVisz564zs4f7e3fvhyGGYIYQYpe7fuhy3+euy0+6nEJMMYiknIdxXC4X69V6moaYQxDGgRN+2PMXL3Hw4TfeAfCfffXzpbR/cvL/4OCzf/JZIzImBZrarqpWNXcYiUiUmGOfu67vutU4Ha+PNpvjzbPTzWZztF6P05SHPnddzEFyijHmnFLOQQTu2CMwy14IQVhADDdVnUspu1Jqm3fb3XZbajXzx+325vrq8vLy7Pz88eGhtlZL2e521pTAKaa+zzFFJkxjf3q6OTk9fXZ6ulituq6PMQCMPQJADneHGtRUm5obiM2sqdbaaqm1lLmUx+32/u7u8urq1ccfvz47Ozu/vLi4ury+eXzYNbf2lX+JN7pvfpGJHd5UtbamaqoOa6qm6gZiEDExYc/gcFMzdwBMxMIkxCxBAouAycwAELMIiwgAd2+q2pqqibCEEENkYVMz1abNzAAwsxwQMxExEQAicndzdzVvqq3VUtWMGcxCRABqbeYKwxMGDAaHg4kAmCsAIiYmJhFmHKiZucLgcAJhTwAwMbm5qQJgoRQ7+ep38caP/uA3UowxJZEgLCAGIEFy30/jtFgu+35IMRGzqgIQkZTS0Pe5y0POIYiZgZiZAJh5KfNuu621mjkL55QkBBHpum61XDLLdretpdRa51Lm3e727u7q8vLm5vb25mY778wcbgDcvakCYBYmEBGImSmGmHMa+mFaTOM49sOQUgohwL3W2lQB+CfMmj5ptTJz7joze7i/d/d+GGIIZgghdrnrhy73fe66/KTLKcQUg0jKeRjH5XKxXq2naYg5BGEcOOHTnr94iYP33n732dc/wgH91i/81drK43a33ZW5tj/9qT/DwV/5zuer2VxardrUAQSmFHIXh9w9WY3T8fro5HRz+uytzeZ4uVpN0xS7LuYUc4xdepJTyjlIdDcc8J48CRKI2B1uWmrV1lqt8zw/bretVFV9fHy8uDg/Oz8/Ozu7v7+vpdQnrTWFWgjSdV3uUtf169W0OTk5PT09OT1ZLlc5JQ5CIBAzCAwzOLypmbm7mmPPzZpqra3WupvLf/x7v4CD//YXfu/169dnexeXFxeXr8+uHh62uy//MQ7CH3yBRZhpD0BTNdWm6mbu3pqaqsMJhDcc7m5uMHcATEQMJoFwCCIhELObgYmYhZhFiMlUm2qr1cyZSURSSsTs5qZaazU3JibmGAMRAyCmPaYn7q5mrmpVW221VocTSJhZ2N1bU3OFYY+E3Q0Gh+PA3QAQMTExiTADcJiauxsMDicQGCAATExubqrj1z7AG6vv/FJOXcpJQoox5xRZIgAH3BGCdLkfF9NyuRqGPqYsLGrGRCISU+r7PscUozCTNSXhFCMzm2Oed48PD/M8mxkz55xjjETc9/1mcxximuddKaXWVubdw8Pjze3N5cXF1dXV7e3dPO/U3N0AuLuZAZA9ZgBEBIBFckrDMC4W0zhN4zDknEUCANXWVN3dPqFaay2lzPNMLEPfufv9/T2AxWIRQmjNmGUYhrEfumnoD3KXc0oxxSCSch6mcblcrtfraRpilCCMAyf8sOcvXuLgw2+8A+C3f/GztZXff/aHOPjsn3zWhR2kQDWbS6tVmzqAwJRC7uKQuyercTpeH52cbk6fvbXZHC9Xq2maYtfFnGKOsUtPcko5B4nuhgPekydBAhG7w01Lrdpaq3We58fttpWqqo+PjxcX52fn52dnZ/f397WU+qS1plALQbquy13qun69mjYnJ6enpyenJ8vlKqfEQQgEYgaBYQaHNzUzd1dz7LlZU6211Vp3c9l7fHx8uL+/uLp6/fr12d7F5cXF5euzq4eHbXNrX/mXeKP75heJCEBTNdWm6mbu3pqaqsMJhDcc7m5uMHcATEQMJoFwCCIhELObgYmYhZhFiMlUm2qr1cyZSURSSsTs5qZaazU3JibmGAMRAyCmPaYn7q5mrmpVW221VocTSJhZ2N1bU3OFYY+E3Q0Gh+PA3QAQMTExiTADcJiauxsMDicQGCAATExubqoAWCTlJL/8XbzxYy9/I8acU2SJABxwRwjS5X5cTMvlahj6mLKwqBkTiUhMqe/7HFOMwkzWlIRTjMxsjnnePT48zPNsZsycc44xEnHf95vNcYhpnnellFpbmXcPD483tzeXFxdXV1e3t3fzvFNzdwPg7mYGQPaYARARABbJKQ3DuFhM4zSNw5BzFgkAVFtTdXf7hGqttZQyzzOxDH3n7vf39wAWi0UIoTVjlmEYxn7opqE/yF3OKcUUg0jKeZjG5XK5Xq+naYhRgjAOnPBpz1+8xMF7b7/77Osf4YB+6ys/XrXeb7fzXOfa/vSn/gwHf+U7n69mc2m1alNnQESSpJyHLuWh65fLxWZ9vDndnD57ttlsVsvVMI0xdymF2KeQY8w5pxRSRyxwPCEQURBmFiECMfbM1NTUTbXWstvtamlN9f7u9uzs448/fn1+fn57e1PmUlo11daaVSWWnMIwjeu9o6OTk83m+Phos1mMY0xJRAhEzCJCTADUXdXUHHsOd1NTbVpqK6XUUv7D/+UrOPjvf+kfnJ+dn19cnF9enp9fvnp9cXf/8Pjut3AQ/r9/j4mZaQ+AqpmqmTnc3N1MtZnB4e6GPYPDAbibG4ixR8TYY2KRKIFEsMcURFiYRQCYam2t1apmBAoiMUYWBqCqpRQzF2EJIYZAzG4OgJj2hNndW1NXbbVZa7U1dyNiYWIWN2+mruZwApEwAHeDYc/hOCAQCQsTswBwczUzVxieMIjY8YSY3Nzdhl/7Ht5Yvv9LXdfFmCSmFGNMSSS4Yc/gIpJzPy6m5XI1jUNOvcQAdxCFEHJKOecYAtz21FSYc9fFEJi51Hp3dzfPs5kxc845hmDuOeflchljVFV74rXM9/cP1zfXF+fnV9fX93f387xrqq5urmbYYwaLMAsT9tRMmFNM/TgsFsvFNA7jmHMOEojJzZqqmamqqdZaSynzk0JM0zgy83a7Y5H1chlTak2ZZeiHfui7oe/6oUspdzmmGIIEkZRTP06r5WJ9dDQNQ8wShHHghB/2/MVLHHz4jXcA/PZf/0zV+vtv/SEOPvf+54zJQQpUs7m0WrWpMyAiSVLOQ5fy0PXL5WKzPt6cbk6fPdtsNqvlapjGmLuUQuxTyDHmnFMKqSMWOJ4QiCgIM4sQgRh7Zmpq6qZaa9ntdrW0pnp/d3t29vHHH78+Pz+/vb0pcymtmmprzaoSS05hmMb13tHRyclmc3x8tNksxjGmJCIEImYRISYA6q5qao49h7upqTYttZVSaim7ueweH27v7q9vrs7Pzs8vLs4vL8/PL1+9vri7f2hq5cvfxhvdN79IRABUzVTNzOHm7maqzQwOdzfsGRwOwN3cQIw9IsYeE4tECSSCPaYgwsIsAsBUa2utVjUjUBCJMbIwAFUtpZi5CEsIMQRidnMAxLQnzO7emrpqq81aq625GxELE7O4eTN1NYcTiIQBuBsMew7HAYFIWJiYBYCbq5m5wvCEQcSOJ8Tk5u4GgIhTTvLL38UbP/qD30gxxpREghv2DC4iOffjYlouV9M45NRLDHAHUQghp5RzjiHAbU9NhTl3XQyBmUutd3d38zybGTPnnGMI5p5zXi6XMUZVtSdey3x//3B9c31xfn51fX1/dz/Pu6bq6uZqhj1msAizMGFPzYQ5xdSPw2KxXEzjMI455yCBmNysqZqZqppqrbWUMj8pxDSNIzNvtzsWWS+XMaXWlFmGfuiHvhv6rh+6lHKXY4ohSBBJOfXjtFou1kdH0zDELEEYB074tOcvXuLgvbffffb1j3BAv/ULf7W28rjdbUuZZ/3Tn/ouDn7sOz/R1HZVtao5iCAsKeQc+y53fd+vFtPm6OT0ZHP61rPN8fF6vR7GMeYUcoo5xi7FnENMJJFZDE+EQCTEEBIiMA4cBsAN5rVpnXe1ttbq7e3dq1cfvXr16uzs7Pb2bt5ta61qbq3W0giIKS0Wi9O3To+PT443x8dHR6ujo2HoY0xBBERBmEMQZiIyd1U1g7m5OxxNVVsttZVS5nn3H/2vv4CD/+4Xf+/i/Pzi4vLi8urs4vLV68u7h4eHL/0RDuI/+2lhpj0QAHNTM3fHnrnDm6qpurmamSsMDgfgbgCIGG84wEIxJhYBEzHHEIhZmM3dVGtr2pqqARBhCUGIATTVUou7xRAlhBgiMbmZuxMxCwuzu7fWtDWtzapWrTCQsDAxi5tXrW7ubkRMTETsbjCA8cQABhELE7MQEQBTUzOHuxsAJgHD8YQAB0x1+LXv4Y3p/V/MOceYYogxppgyC8MZBxwk536apsVyMY1T1/cxRBAxcwwhPkksZK2ptj0m7oc+7cWoqg8Pj7UWM2ORnBIRO5yZu5xDCNgjAlBLubu/v7q6ujg/v7q+fnx4mOdZzfTA3YlImFmEWZhgDjMlohRi3/eLxWJaTOM45q6LIYAI7mpvqNbWailzqbUWYRnHgZjLPMcYF8tlztkNUWLuu9x1ucsxfSLGICwiJLlL/Tgtl9PR8fE0DDFLEMaBE37Y8xcvcfDhN94B8Nu/+Nnayu8/+0McfO79zxmTAQ1oaruqWtUcRBCWFHKOfZe7vu9Xi2lzdHJ6sjl969nm+Hi9Xg/jGHMKOcUcY5diziEmksgshidCIBJiCAkRGAcOA+AG89q0zrtaW2v19vbu1auPXr16dXZ2dnt7N++2tVY1t1ZraQTElBaLxelbp8fHJ8eb4+Ojo9XR0TD0MaYgAqIgzCEIMxGZu6qawdzcHY6mqq2W2kop87yb53m73d7f3V1dX1+cn19cXF5cXp1dXL56fXn38KBqu5/7Ft7o3/sSgQCYm5q5O/bMHd5UTdXN1cxcYXA4AHcDQMR4wwEWijGxCJiIOYZAzMJs7qZaW9PWVA2ACEsIQgygqZZa3C2GKCHEEInJzdydiFlYmN29taataW1WtWqFgYSFiVncvGp1c3cjYmIiYneDAYwnBjCIWJiYhYgAmJqaOdzdADAJGI4nBDhgqgCIkGInX/0u3vjL338RY4opszCcccBBcu6naVosF9M4dX0fQwQRM8cQ4pPEQtaaattj4n7o016Mqvrw8FhrMTMWySkRscOZucs5hIA9IgC1lLv7+6urq4vz86vr68eHh3me1UwP3J2IhJlFmIUJ5jBTIkoh9n2/WCymxTSOY+66GAKI4K72hmptrZYyl1prEZZxHIi5zHOMcbFc5pzdECXmvstdl7sc0ydiDMIiQpK71I/TcjkdHR9PwxCzBGEcOOHTnr94iYP33n732dc/wgH9J7/8uVrLw2633c3bUt//ye/i4Eff/2xVL1XNzQ0kkiRGTjGmLnV91y+Xi9Pj45OT09O3nm02m9VqNU5Tzl3MKfYp5hRyEg7K7CAQE4FAvEcEYgIYIIBwQNhz1VJKq6VVvbm5+uj733/16uXrs7Pbm5vtdjuXAkNTbaUASCkt16u33nnn9OR0tXe0Xq9W3TCmIBKCiAQRDjEIEbE7mqqrNlM3U3Pda7WWVkqZ591v/sNfxsF/83P/88XF+fnF5fX19dnFzfnV9cPD9vZn/jkOum9+kfZAYMKeucP3ABCRu7fazKypultraq4wOBxvEAiAwx0gphgji5BIEJEYhBmAmplqU9XW3B0AETEzQMRkTecyA4gxhhhTSkSkrbk5MbFIEHH3WmurzWtTba2qw4mJSYTZYa3qnrkzkYgAcDiBSJhAOBBmFgaImNzc3cxUzXEgTNhjxoE90f5Xv4c3hm9/JeccQ5K4l1KMLBFEBGJmiSF3/TiOy+VqGse+H2JKIhLkSQhBQmCgtaqtzqUwcdd3fe5yl0FcazEzAMwcY2JCa6qmZgYgiBAxM82l3N/dXV1evT4/v7u9eXjctlrUzNWbqrkSkTCzCLMwwRxmSkQhxL7Lwzgupmkcx9x1MUYiMjO4EzMAtyfatJmaGTPnnJmotcYi0zjm3OeUYupySjEFDjGISBBmCYGImYi61A2LYbVarY+OpmmIUYIwDpzww56/eImDD7/xDoD/9Fc+X2v5Jyf/Lw4+9/5nFWRAg1f1UtXc3EAiSWLkFGPqUtd3/XK5OD0+Pjk5PX3r2WazWa1W4zTl3MWcYp9iTiEn4aDMDgIxEQjEe0QgJoABAggHhD1XLaW0WlrVm5urj77//VevXr4+O7u9udlut3MpMDTVVgqAlNJyvXrrnXdOT05Xe0fr9WrVDWMKIiGISBDhEIMQEbujqbpqM3UzNde9VmtppZR53s1lfnx8vL27u7q8urg4P7+4vL6+Pru4Ob+6fnjYtqaP7/4R3hje+1kwYc/c4XsAiMjdW21m1lTdrTU1VxgcjjcIBMDhDhBTjJFFSCSISAzCDEDNTLWpamvuDoCImBkgYrKmc5kBxBhDjCklItLW3JyYWCSIuHuttdXmtam2VtXhxMQkwuywVnXP3JlIRAA4nEAkTCAcCDMLA0RMbu5uZqrmOBAm7DHjwJ6oqRNTjCn++/8ab7zzwa/HmFKMLBFEBGJmiSF3/TiOy+VqGse+H2JKIhLkSQhBQmCgtaqtzqUwcdd3fe5yl0FcazEzAMwcY2JCa6qmZgYgiBAxM82l3N/dXV1evT4/v7u9eXjctlrUzNWbqrkSkTCzCLMwwRxmSkQhxL7Lwzgupmkcx9x1MUYiMjO4EzMAtyfatJmaGTPnnJmotcYi0zjm3OeUYupySjEFDjGISBBmCYGImYi61A2LYbVarY+OpmmIUYIwDpzwac9fvMTBe2+/++zrH+GA/u6vfL60ev+43ZXd466+/5P/Cgc/+iefKWa1urkBTCQpxiRROKaYx75fLpcn6+OT09Nnz043J5vVej1OUzf0qetiDjEniclZGvYYTEzMJERgEiIQAAIBDBCBABCgVlurtbRSr68uP/zwg5cvX56fnd1c39w/PtTSALjqXHYAcurWx+u3f+T55uR0tbderVbrYRhjCjFEEWGRIEJMTAw3NTdVNbUnrqqmWmvZ283lN//BL+Lg7/3s/3h2dnZxcXl1fXNxdXN5dfu429389D/HQf/el4j0gLH3AAAgAElEQVSZiHDg7niDiNzdVJuqNW2qptpMXc3h+BQCOeBwFmKWEGOIQcITIlIzU1Ntqmpm7k5EAAhETABUdZ5nADGmlNMeEbVS1U2IWSTG4O5lLq02a09MFQAxMYkwO6xV3TN3JmJhAmGPEUQIDICYnjAzEQBzdzPVZgaHE4gZxAwiAgFo2sy0/9Xv4Y3uWz+fcwoxhZBiiCkllsgse0wUU8x93w/TcjEN4zQMQ85dCEFEmFlYWJgB0706z4WYUkpd1/VdxyKmauYAWCQGAZGZq2qtxcxiCMwMYLvb3d7eXl1enr0+u7m92e7m1qq7w9BUHUZgFuInwgRzuBuAIJJz1w/9MAzTOPZ9H1NiZjNjIhYhIj/AAREJ7wkAM2XmnHNMOaecUxdjkBhZhEWEiWWPgwgxdV03TtNytdwcHw/jEAOLEA6c8MOev3iJgw+/8Q6A3/nqXyut/uPjf4qDz73/OQUUULdiVqubG8BEkmJMEoVjinns++VyebI+Pjk9ffbsdHOyWa3X4zR1Q5+6LuYQc5KYnKVhj8HExExCBCYhAgEgEMAAEQgAAWq1tVpLK/X66vLDDz94+fLl+dnZzfXN/eNDLQ2Aq85lByCnbn28fvtHnm9OTld769VqtR6GMaYQQxQRFgkixMTEcFNzU1VTe+Kqaqq1lr3dXObd9v7h4fb29ury8uzs7OLi8ur65uLq5vLq9nG3a2oPX3oPb4zf+jkcuDveICJ3N9Wmak2bqqk2U1dzOD6FQA44nIWYJcQYYpDwhIjUzNRUm6qambsTEQACERMAVZ3nGUCMKeW0R0StVHUTYhaJMbh7mUurzdoTUwVATEwizA5rVffMnYlYmEDYYwQRAgMgpifMTATA3N1MtZnB4QRiBjGDiEAAmjYzNXUAEkP3N/8cb7z1b/5WDDGlxBKZZY+JYoq57/thWi6mYZyGYci5CyGICDMLCwszYLpX57kQU0qp67q+61jEVM0cAIvEICAyc1WttZhZDIGZAWx3u9vb26vLy7PXZze3N9vd3Fp1dxiaqsMIzEL8RJhgDncDEERy7vqhH4ZhGse+72NKzGxmTMQiROQHOCAi4T0BYKbMnHOOKeeUc+piDBIji7CIMLHscRAhpq7rxmlarpab4+NhHGJgEcKBEz7t+YuXOHjv7Xefff0jHNDf/ZXPl1bvH7e7Mm9L/ZOf+A4OfvT9zxbVUtUUALNIDDFJYIopprHrl8vlZn20Odmcnp5uTjbHx8fTctmPQ9d3kpNE4RCMRZ1BBGaGiBATk4gQERgEgjFAhD1ydzNVrbWUeb68OP/ge9/76Ac/uDy/uLq+erh/bKUQw1VLqWDkbjg6OnrrnR/ZnJwsV6vlar0+OlpMU84pxUgsRCIMEPbcYaYOwM33DGb6pNVSyjzP/8HvfgUH/+UX/4fXr1+fn19eXl1eX99d3t5vd/Ptz3wTB8N7P0vCRIQDd8cbROTupntmbta0qZpqM3U1c8UbREwgMEDMTDHFGJOkGFgc3lS1tqbNzHBAIHxK1TbvCoDcpZxzSomIaimqJsIiEmN093meW21WWtNqqgBYhElCEDevWk3VDcRgESIGQKAQhIhxIMIEIiYAZqZqpqpmDieQMBMTiIgJgKrWWodf+x7eSP/i59JezDGmIDHlKCGyBBEJIaSUur7v+nExTcNBzl1MKYiACACBmEmE3KzWCneREFPMKTGzmbm7uQszS2AmYQagZgCI2N3M/PHx4fr6+ury8uPXr2+vbx53W1MFEfbczcAMEPETAcAEc+wJUwyxH/qu78dxGvou58wiAJg5hgAiuGOPSERyzkykquaON5j2wl5KKYQYUpAQo0hIIcaYYggS85AXi+VqtTw+Ph7HXoSI8Akn/LDnL17i4MNvvAPgd77610qr//j4n+Lg8+//RAUM3uBFtVQ1BcAsEkNMEphiimns+uVyuVkfbU42p6enm5PN8fHxtFz249D1neQkUTgEY1FnEIGZISLExCQiRAQGgWAMEGGP3N1MVWstZZ4vL84/+N73PvrBDy7PL66urx7uH1spxHDVUioYuRuOjo7eeudHNicny9VquVqvj44W05RzSjESC5EIA4Q9d5ip//9swVuM7el5J+Tfe/i+/2Gd6rj39m67ux3HcduOQ5JpexIYkiHySBk0PSRhNLEECAZFGSGBhMQVV5HgkmvEhQVcII0Qt77hhhuEhIg7djzT7tiaON129967qlatOq6qtdb/+973ZdVql9RW5nkAhMeWw90e1DIMw2azuV+tlsvl5eXlxWJxdnZ2fn5xcXlxdXV7cbNcrTfV4/4338Wj8V9+AzsRgUdEFBFuW+7hXq2auVl1C3MPwyMiJhAYIGamlFNKWXJSlkBUMyu1WnV37BAIn1KsbtYDgKbNTdPknImoDIOZi7CIpJQiYrPZ1FJ9qNWKmwFgESZRlfAoVtwsHMRgESIGQCBVIWLsiDCBiAmAu5u5m5l7IAgkzMQEImICYGalFHcDICl3v/cTPHr6N7+vknKTRBOLioiq5pzbrmu70WQ87neapk05qwiIABCImUQo3EspiBDRlFOTMzO7e0R4hDCzKDMJMwBzB0DEEe4e9/d3V1dXlxcXp2dnN1fX9+uVm4EIWxHuYAaI+IEAYIIHtoQpaer6ru260Wjcd23TNCwCgJmTKogQgS0iEWmahonMzCPwiGlLt3LOqkmziqYkollTSjmpSmr6ZjKZzmbTg4OD0agTISJ8Igif9to7r7Dz7rO3n3z7BXbon/+7XxrKcL9er9bDUOsP3nofO6+/96VidShmhi2CpKTCSUmblNu+m40ne3t7RwdHx8eHh0dHh4eHs71ZNx43XZeyQAUsTgIWMIGFiYWERVhEmYiYCFtMQIDCEVtuZqXUzXo1n89/+sFPXr54uVicX19dLZfLYShCHG6lDBDp+9Fsf+/46bODw6PZdLZ3sH9wcDiZTPu+S6rMjC2HwyIQ4RGBwCc8HB7m5rWUoa6G9R/9j38XO//1r/4PZycn8/l8cXl5dbO8Xa5XpSx/413s9O/+HRImIuzEDgDaiS1zD3cPN3P3arWU6mYeZoYtJrAwMYGYmZhZVHPbpJSICY6hDKVWq9XMmYmJiQlAeAQCQKllsx4ANG3b5Ny0DRGVYTBzERaRlFJEbDabMhQrxYttARARFlEVAKXWMA8EgVhEmAEQE4swEXaYmUBggoe7W7iVau6BIJAwkxCBwATAzEop/T/4EI/Su19v2iannFJOO6pZRSWnrDnl3PRd23ajvuv7vutGTdM2TWYRbEW4OwBhiohqFYCKJFVRZZCFh0cgmLnJOT3IIoIdc3OzUu3+/u7q6ury4uL07Ozq6vr+/q7WSlvggBOYGSBiZnrATPgEESVNOee270Z933Vd27YpJZYHKuIRtdaIIKKcUt+PWLiUgggRBWBW3QNgUc05p62cU9KkSXNusqackmpu28l4PJ1N9/f3RqOemUQIO0H421575xV2fvadzwD4L3/ny0MZ/s+j/wc7X37/LQMZongUq0MxM2wRJCUVTkrapNz23Ww82dvbOzo4Oj4+PDw6Ojw8nO3NuvG46bqUBSpgcRKwgAksTCwkLMIiykTERNhiAgIUjthyMyulbtar+Xz+0w9+8vLFy8Xi/PrqarlcDkMR4nArZYBI349m+3vHT58dHB7NprO9g/2Dg8PJZNr3XVJlZmw5HBaBCI8IBD7h4fAwN6+lDHU1rO/v729ubi4vL+dn87OTk/l8vri8vLpZ3i7Xq1Lc4v7tv8Cj8V9+AzuxA4B2Ysvcw93Dzdy9Wi2lupmHmWGLCSxMTCBmJmYW1dw2KSVigmMoQ6nVajVzZmJiYgIQHoEAUGrZrAcATds2OTdtQ0RlGMxchEUkpRQRm82mDMVK8WJbAESERVQFQKk1zANBIBYRZgDExCJMhB1mJhCY4OHuFm6lmnsgCCTMJEQgMAEws1KKuwEQyd03f4JHT//m99OOalZRySlrTjk3fde23ajv+r7vulHTtE2TWQRbEe4OQJgioloFoCJJVVQZZOHhEQhmbnJOD7KIYMfc3KxUu7+/u7q6ury4OD07u7q6vr+/q7XSFjjgBGYGiJiZHjATPkFESVPOue27Ud93Xde2bUqJ5YGKeEStNSKIKKfU9yMWLqUgQkQBmFX3AFhUc85pK+eUNGnSnJusKaekmtt2Mh5PZ9P9/b3RqGcmEcJOED7ttXdeYefdZ28/+fYL7NB//lufH8zW6826DEO1f/XWX2Hnl95/qxRbl1LN3ADIlkoS0TY1bdtNx+PZbO/g4OD46PDw6Oj46Gi2vz+ejJu20ywQqgGwkCZRBTORsGypiqgIszAxkQMID7h5INzgKGVYre7Ozs4++Ou/fvny1WJxfn11dbtclk1RASJKKSLSTybTvb3D4+PDg8Pp/v7hwcHh0dHebNb3o5QztsK9mlmtbuHuEQgwHngA4e5hW7WsN5s/+p9+Czv/xRf/+5OXr07O5ouLxc3t/brYUOz+7e9hp3v3N4mZdmIHHvgEE7Y8ArFl7mFerFqpxYoVswjsqBCLsAgzExGLNn2bkhJxmG+GTSllKCXCmURFiBlAuAdiq5a6HgYAbc5N2zZtQ0SbzSbciTmpppQiYrPZlPVQS3WrtRoTiYgmEVEAZtU8ABAoJSVmJgJAIOwQE4HAtBVb7tXMzWq1QBCIGcwCImICYGallP4ffIhH+XvfaHNOTZtSzppzk5JmFk055a1mq23armmbvuu7rmuaNuWsIh7h7lYfmJm7AcHMSZOIqjIAN/MIAKra9/2o7/uuTzlFwNysWqmllLpa3d/c3FxeXJyenV1dXt4ul2UoEY4dIklJmAQMesBMIAJAIBaRnHPXNm3Xjfq+bdumbZucRRWA1bparUopHtHkPJ1OVXUohYm7vmPmWop7MItIym1OOaeUU9ImZ9WUsiYVFkk59303Ho/3ZrN+3DdNUmHsBOFve+2dV9j52Xc+A+Cf//YXBrP/6+n/i52vvv+WM1fA3EuxdSnVzA2AbKkkEW1T07bddDyezfYODg6Ojw4Pj46Oj45m+/vjybhpO80CoRoAC2kSVTATCcuWqoiKMAsTEzmA8ICbB8INjlKG1eru7Ozsg7/+65cvXy0W59dXV7fLZdkUFSCilCIi/WQy3ds7PD4+PDic7u8fHhwcHh3tzWZ9P0o5Yyvcq5nV6hbuHoEA44EHEO4etlXLerO5u7u7urk+Pz8/Oz09efnq5Gy+uFjc3N6viw3FArF6+/t4NPr+14koduCBTzBhyyMQW+Ye5sWqlVqsWDGLwI4KsQiLMDMRsWjTtykpEYf5ZtiUUoZSIpxJVISYAYR7ILZqqethANDm3LRt0zZEtNlswp2Yk2pKKSI2m01ZD7VUt1qrMZGIaBIRBWBWzQMAgVJSYmYiAATCDjERCExbseVezdysVgsEgZjBLCAiJgBmVkpxNwSLavfNn+DRsw///aw5NylpZtGUU95qttqm7Zq26bu+67qmaVPOKuIR7m71gZm5GxDMnDSJqCoDcDOPAKCqfd+P+r7v+pRTBMzNqpVaSqmr1f3Nzc3lxcXp2dnV5eXtclmGEuHYIZKUhEnAoAfMBCIABGIRyTl3bdN23ajv27Zt2rbJWVQBWK2r1aqU4hFNztPpVFWHUpi46ztmrqW4B7OIpNzmlHNKOSVtclZNKWtSYZGUc9934/F4bzbrx33TJBXGThA+7bV3XmHn3WdvP/32i8AD+o/+zmvVfFNKGUox/1df+RF2fvn9Lw9mm00tZm4ezsxEIkly0zSjbjSdTA72Dg6ODo+3Dg/3Dw+m02nX96nJokJMBmYRyVmSgoWIWURYVR6wiDAT4UEg3NwRbhEow3B3vzyfzz/84IOXL14szhdXVxfL22UZBsARcDPV1I9H0729/cODw6PjraOjo8PDo8l00ratqgKIatWqm7lbhGMr4B4IB+AR4V7NylBX69Uf/8+/g50//fyfvXx1Mj+bLxaLm7vVpnqpcf/2X2Bn9P2vExETtjzg7vAIBB6ZeSCwExFerVotpZpZrRYIAqkyi6SkxAxARFPbaBIicbNh2AybYagDHCSiwswCENwdXs2t1FKNCDlpk5vcZBCVoQSCmFVEVCOilqEMpW6KW3VzACLCIqpCRG4ecADMoqrMTCAAgcAOgbDFtBVb/sAeVHdsMYPAJAwQAA+rtXTf/BCP2u9/o226pm1zzinllFsVIRFVbXJOuc1NbpqubXPX9VtN0zVNZmb3qNXMarVq1cxqNWOmpElVRRiAu7k7Aqradd1oPJpMJl3TElEgbKtaKcPybnl5cXV1dblYLC4vL6+urtbrjYczQCzMLKIsxACY6QEzgQgAsUjS1LTNqO/abtT3bZMbTSmpMnM1W9+vhlrCPaU0Ho+FtVhVkb4faVI3AyCaNKWUc8o56VZKKillUVEVZmlybvtmNBrPZtN+1HdtUhXsBOFve+2dV9j56DvPAPynb79ezf/vz76Lna++/5YzG+Aeg9lmU4uZm4czM5FIktw0zagbTSeTg72Dg6PD463Dw/3Dg+l02vV9arKoEJOBWURylqRgIWIWEVaVBywizER4EAg3d4RbBMow3N0vz+fzDz/44OWLF4vzxdXVxfJ2WYYBcATcTDX149F0b2//8ODw6Hjr6Ojo8PBoMp20bauqAKJatepm7hbh2Aq4B8IBeES4V7My1NV6dXd3d3lzvVgszs/OXr46mZ/NF4vFzd1qU73UAOL+7e/h0fQH3wDgAXeHRyDwyMwDgZ2I8GrVainVzGq1QBBIlVkkJSVmACKa2kaTEImbDcNm2AxDHeAgERVmFoDg7vBqbqWWakTISZvc5CaDqAwlEMSsIqIaEbUMZSh1U9yqmwMQERZRFSJy84ADYBZVZWYCAQgEdgiELaat2PIH9qC6Y4sZBCZhgAB4WK3FDQSWlJrf+zEefe7jd1LKKbcqQiKq2uSccpub3DRd2+au67eapmuazMzuUauZ1WrVqpnVasZMSZOqijAAd3N3BFS167rReDSZTLqmJaJA2Fa1Uobl3fLy4urq6nKxWFxeXl5dXa3XGw9ngFiYWURZiAEw0wNmAhEAYpGkqWmbUd+13ajv2yY3mlJSZeZqtr5fDbWEe0ppPB4La7GqIn0/0qRuBkA0aUop55Rz0q2UVFLKoqIqzNLk3PbNaDSezab9qO/apCrYCcKnvfbOK+y8++ztz3z7Y8cD+idfO67htZqZV/P3vvJj7Pzye18288FqsS24Y0dUpG278Wh8cHDw5Mnx0yfPnj59erC/P51OmrYTVWZhJWJRFclJm0ZSYiYQCwuzEAkxiTCzkAgTERjhFhHu4TEMw9398uL84uXHH7189Wp+erK4uFje3m5W6zCLCCJKKbV9N51O9g4Onjx79vrrnzt+8mQ2nfVdxyJC5Igwq6UgAgwhImY4ahnMHQSPsGrDMKw2w/39/X/2L34fO3/y5p/Nz+Zn5/PFxdXt8n69KSVi+evfxc7+e7+NnQi4m3mEeSC23CMQVmogCERMBALg7tXcrFYr4QGARZKq5sTM4SBm0STKROzhZTOUUoZawg1gElZmJgZzuG2GTS0WgDCLiIpKUmFyD4CYaYuZAnAzq7UMGy/mYQCImECahFkAEBEAYhbiLTBhyyMQBMIWEx6Fe3hUswh3N3c8YDCEmMLh8K3umz/Bo/4Hv922Xd/3TdPk1EpSYfGAiOScRHNKOTW5bbvxqB+Np6O+TzkzsbvV6hEOgIiLlfu722qWNacHCiar5m7uzkSi2rXdZDIZjfqmbZImJrjZZhiur69PT08uLy9vb++ury7n88VqtYSDRFJKIgQQAGaAmEFgJsInVERU29z2437Uj/q+E1EzE9WubVmk1uruAJhYNRGRmYtK17YpZWZKqqltU1ZNSURZhMEiwiIpiaio5qbJ474fT8bTybQfd02jKoxPED4lsPP8nRPsvPjOEwD/9Nee1/D/781/iZ2vvv+Wgxwwh5kPVottwR07oiJt241H44ODgydPjp8+efb06dOD/f3pdNK0nagyCysRi6pITto0khIzgVhYmIVIiEmEmYVEmIjACLeIcA+PYRju7pcX5xcvP/7o5atX89OTxcXF8vZ2s1qHWUQQUUqp7bvpdLJ3cPDk2bPXX//c8ZMns+ms7zoWESJHhFktBRFgCBExw1HLYO4geIRVG4ZhtRnu7+/v7u9vbm6vrq4WFxfzs/nZ+XxxcXW7vF9vSolg0M2v/zke7b/32xFwN/MI80BsuUcgrNRAEIiYCATA3au5Wa1WwgMAiyRVzYmZw0HMokmUidjDy2YopQy1hBvAJKzMTAzmcNsMm1osAGEWERWVpMLkHgAx0xYzBeBmVmsZNl7MwwAQMYE0CbMAICIAxCzEW2DClkcgCIQtJjwK9/CoZhHubu54wGAIMYXD4VsACJJSkt/9IR594ew/zKmVpMLiARHJOYnmlHJqctt241E/Gk9HfZ9yZmJ3q9UjHAARFyv3d7fVLGtODxRMVs3d3J2JRLVru8lkMhr1TdskTUxws80wXF9fn56eXF5e3t7eXV9dzueL1WoJB4mklEQIIADMADGDwEyET6iIqLa57cf9qB/1fSeiZiaqXduySK3V3QEwsWoiIjMXla5tU8rMlFRT26asmpKIsgiDRYRFUhJRUc1Nk8d9P56Mp5NpP+6aRlUYnyB8Sjx/5wQ77z57+zPf/hl26A9+9cDdzdw8zOK9r/wYO7/83pc9vFQvZlbdHOEAc0qpbbvJZLy/f/js6bOnT589e/Jk72B/PBrlnIWZWJhFsqScU1ZNiVVABGKAmBgEItkikaSZRIQIQESYOyI2m2G5XC4WFycvX5ycvDo9Obm4uLi7XW7WKyvFIpQopdS07WQ62T88ePbaa5//pc8/e/J0PB43OQEEBMLdrJSKcBFWEWEyjzoM5gYij6hmQymr9eZ+tf5n/+IfYudP3vyz+Xx+dr5YLC5ulvebTSkRy1//Lnb23/t38MDdwsK3wt3MA/HAvZqFBzExETMTCIC7VzM3q24AlIWTpJSIBA+YkzBtwTxqKbXUYtXNARCTELMwMbvZZrOpZgSICLOwsDAzMfEWASAiABEBwGq1YahWwiPCiZhAmoRZiJiYADARgYiJmAGEe3gQEwAC4VEgwsPCwyPCwyPgAAMMwBEAwqP95r/Go/F7f69vu7btm7bLOSfNIFiACZqyirDk1OS+6/rRaDad9X2fcmZmNzN3AMKsKdVSrm+uhlJzymkrK4isVjdzd0SAqMnNeDIej0fjftx0WUU8fFhvri4vPn7x4mKxuF+tbq6vz+bz1d0KcGbJuVFhR+ARM4EYcAYDIBFV7dq27/uuH/Vdq6pmrimNRqOkWs3CHT/HCHiEiHRtm3JW1ZRTyjll1ZREBCRMAJHKVspZU0pN241H/WQynk6m/bhrGlVhfILwKIDAzvN3TrHz4jvHAP7oa8/c/bu/9EPsfPX9txzkgDk8vFQvZlbdHOEAc0qpbbvJZLy/f/js6bOnT589e/Jk72B/PBrlnIWZWJhFsqScU1ZNiVVABGKAmBgEItkikaSZRIQIQESYOyI2m2G5XC4WFycvX5ycvDo9Obm4uLi7XW7WKyvFIpQopdS07WQ62T88ePbaa5//pc8/e/J0PB43OQEEBMLdrJSKcBFWEWEyjzoM5gYij6hmQymr9eZ+tb6/W93c3l5dXS0uLubz+dn5YrG4uFnebzalRDDRzb/153i0/95vu4WFb4W7mQfigXs1Cw9iYiJmJhAAd69mblbdACgLJ0kpEQkeMCdh2oJ51FJqqcWqmwMgJiFmYWJ2s81mU80IEBFmYWFhZmLiLQJARAAiAoDVasNQrYRHhBMxgTQJsxAxMQFgIgIREzEDCPfwICYABMKjQISHhYdHhIdHwAEGGIAjAIQHMQuzaKK/9y/x6Ivn38o5J80gWIAJmrKKsOTU5L7r+tFoNp31fZ9yZmY3M3cAwqwp1VKub66GUnPKaSsriKxWN3N3RICoyc14Mh6PR+N+3HRZRTx8WG+uLi8+fvHiYrG4X61urq/P5vPV3QpwZsm5UWFH4BEzgRhwBgMgEVXt2rbv+64f9V2rqmauKY1Go6RazcIdP8cIeISIdG2bclbVlFPKOWXVlEQEJEwAkcpWyllTSk3bjUf9ZDKeTqb9uGsaVWF8gvAogHj+zil23n329vNv/zTwgP7ga4fhXm0rLPDeWz/Czhff/7K51+pDNatePcLBql3bdV3f9d1stnd8ePyJ/b396Xjc9Z2IatL8iTazCjM5MWAeMEfggbCI5qQqKWtKKgJQRHgg3Debzc3NcjGfv3j58emrV/Ozs8urq/XybrPZ1FrcjQEVSU0znU2Pnj797Gdfe/PNN4+ePB33Xc4ZcEQQ4FbKUBEuzMJExBE+DNXdwAiQI8x8KHW9Hr71v/572PnTX/rvTk5OTs/m54vF7e1yXbxa3L/9F9iZ/eC3AATcPMLcw809zLcABMLMsUNMQkxMBAqEu5t5hAOgLZGkQiJEwmAIAfAId7f6wM0tnIgYRExbAKpZGQZzJ0BEmJmYVYSJwUQgZsKOewAItzKsvVggABCIRVSFRZiIQIHAIwIBCIRHMBEeEQg7gQDgEQDCAzsBRCA8ADjQ/N6P8Wj63u90fde0XZPbnJukCQQLbCUREmGRnJu26ybj8WS61/d9zlmYzcw9mElENOVqdXl7W6w2KYuqigJRrZiZuyOCiVLK4/Go6/vxeNQ1TcoJgfVmdbFYfPzRR+fni7u75c3NzWJxuVnfA8xMKSURYQIY5hGBLSJsEYgZLJI0N01uu65ruq7vmqZJKTdt27WdCG+GwWr1rQBACMBDk7Ztl9smJWURYhKVnBNLEhEmAjglSSlr0qSp67p+1E8nk9ls2o/6rk2qjE8jAAEEHsTzd86w83K35pAAACAASURBVPI7RwD+8Neeh/uff/497Hz1R285yB0OmHutPlSz6tUjHKzatV3X9V3fzWZ7x4fHn9jf25+Ox13fiagmzZ9oM6swkxMD5gFzBB4Ii2hOqpKypqQiAEWEB8J9s9nc3CwX8/mLlx+fvno1Pzu7vLpaL+82m02txd0YUJHUNNPZ9Ojp089+9rU333zz6MnTcd/lnAFHBAFupQwV4cIsTEQc4cNQ3Q2MADnCzIdS1+vh7v7+5ub2/OJiPj8/OTk5PZufLxa3t8t18WpBQne/8S4eTX/wDfMIcw839zDfAhAIM8cOMQkxMREoEO5u5hEOgLZEkgqJEAmDIQTAI9zd6gM3t3AiYhAxbQGoZmUYzJ0AEWFmYlYRJgYTgZgJO+4BINzKsPZigQBAIBZRFRZhIgIFAo8IBCAQHsFEeEQg7AQCgEcACA/sBBCB8ADgAINFRJP6v/2XePTF82/l3CRNIFhgK4mQCIvk3LRdNxmPJ9O9vu9zzsJsZu7BTCKiKVery9vbYrVJWVRVFIhqxczcHRFMlFIej0dd34/Ho65pUk4IrDeri8Xi448+Oj9f3N0tb25uFovLzfoeYGZKKYkIE8AwjwhsEWGLQMxgkaS5aXLbdV3TdX3XNE1KuWnbru1EeDMMVqtvBQBCAB6atG273DYpKYsQk6jknFiSiDARwClJSlmTJk1d1/WjfjqZzGbTftR3bVJlfBoBCCCev3OKnXefvf382x9ih/7ga4cRXqtZRHX88Es/ws6vvP9lcy/VS/X6wAMgzm3XdU3bdO10Mj3Y2z84PD4+PjzY359MpqNR3zZNzrltuqbNqc2qAiZHuFv1rYjAFpOmJqfUpiZryioCUESYw83uV6urq+vzs9MXLz4+PTlZzOe3N7fr9apshmolLAjOIimnvb29z7z2/LOf+9wbb7x5dHTYd11KCXBEAAgrQ6lwY9piJrh7qTXcHQggwG5e3dab4Y//l7+PnT95889evjqZn83PLy5vlnebYu5x//b3sDP9wTcAeMC3zMw9zLcsHDvhgUfEJMTERKBAAHB37DAzibCIihJxAB5u5m5WrLpZeAAgJgBEFFse1ayUISKISESYmZiFmIWJmUD4RW61bDZhFggCkbCySFIhJiYA4WHh4QGAmMIDfwsxYYeJ8IhAAAIwi0Bs4QHp3/8rPNp7/3ebrmubNuc25yZpAiHwgIhAzCIp5a7vx+PpbDYb9X1KiZndHQAzi4imVM3u7+/dLOWsoiLs4aUM5ubuABiUNHV91/f9eNS3Xdc2GcBmvV4szn/205+enp0tl8vb69vrm+thsyaw7BCzCoFRzcIBBH6OWFiEc0o55abtmrZrmtx3/Wg06vq+bVpmWq3XVms1QwTAAMFDc+q7LjWNqpJQmEE456QpqSixMLOI5pyS5ibnpmnGk9F4MtmbzUbjvmuTKuPTCEAAAQQQz9+ZY+fld44A/OGvPY/wP3/zPex89UdvORiAeZh7qV6q1wceAHFuu65r2qZrp5Ppwd7+weHx8fHhwf7+ZDIdjfq2aXLObdM1bU5tVhUwOcLdqm9FBLaYNDU5pTY1WVNWEYAiwhxudr9aXV1dn5+dvnjx8enJyWI+v725Xa9XZTNUK2FBcBZJOe3t7X3mteef/dzn3njjzaOjw77rUkqAIwJAWBlKhRvTFjPB3Uut4e5AAAF28+q23gx396vrm+v5fHE2P3v56mR+Nj+/uLxZ3m2KuQeY73/zXTwa/+U3fMvM3MN8y8KxEx54RExCTEwECgQAd8cOM5MIi6goEQfg4WbuZsWqm4UHAGICQESx5VHNShkigohEhJmJWYhZmJgJhF/kVstmE2aBIBAJK4skFWJiAhAeFh4eAIgpPPC3EBN2mAiPCAQgALMIxBYeEBGzSEpa/+738OiL59/KuUmaQAg8ICIQs0hKuev78Xg6m81GfZ9SYmZ3B8DMIqIpVbP7+3s3SzmrqAh7eCmDubk7AAYlTV3f9X0/HvVt17VNBrBZrxeL85/99KenZ2fL5fL2+vb65nrYrAksO8SsQmBUs3AAgZ8jFhbhnFJOuWm7pu2aJvddPxqNur5vm5aZVuu11VrNEAEwQPDQnPquS02jqiQUZhDOOWlKKkoszCyiOaekucm5aZrxZDSeTPZms9G479qkyvg0AhBAPH/nFDvvPnv7+bc/xA79wdcO3MMeeAX98Es/ws6XfvQVqz6Yl2K1Wq3mASKRlB+kZjweT2d7hwcHx0eH+1uzvfFk3HVd27Zd23Ztm5qcsrKKE8zN3apFBECkknJuUtO2udGciAWgiKjVhqEsl8vFxcX85PTFi49OT88uLi5Wy2UtQ6nFLRAODxZKKR0cHHzuzTfeeP2Nz77+uf2Dg7ZtVBhb4UQRZrUU82CACFsRXmuEW3UPD3M3j2K22Qz/8f/2Tez8J8//25OT08X5xeLqenm/KqVWi/u3/wI70x98A4B5hLmHV7PwnYjwAEBMAMIDCIBEeItAYNqKCOzQlrCKbhFJNTO3Wmsp1d3cHAAxMTGAQLi5hXutxQwRIBJmJiIWFhZmJgYTfpGXWssQZgBIWJhEUkpKzEQUEV7NwsM9IrBDRPg3IADExEQACERMBHJEtQh3/BzJ776PR/s//r2myU3T5tTmlFWVmEGEn+Mg5Nx0/WgymezN9rq+TykxsYczETOLCIu4+2q9jogmN6ICwKqVunE3PEqiTdt2bTce933fbQFUNpv5+dlPP/jw5PTk6vr69vb2/u6+DIWZhJmIiUlUAJhZhLk7doiYWbY0SZNy03Yp56ZpRv1oMpn0o1HXtgDWm00txd0BMAlAIFLVvu1S02jWQJRhACPnJuUkklV2VJI2Oecmp6brxqPxdDqezWbjcdc0qsr4NAIQQAABxPN35th5+Z0jAH/4a59xj+9+/j3sfPVHXwaRBxyw6oN5KVar1WoeIBJJ+UFqxuPxdLZ3eHBwfHS4vzXbG0/GXde1bdu1bde2qckpK6s4wdzcrVpEAEQqKecmNW2bG82JWACKiFptGMpyuVxcXMxPTl+8+Oj09Ozi4mK1XNYylFrcAuHwYKGU0sHBwefefOON19/47Ouf2z84aNtGhbEVThRhVksxDwaIsBXhtUa4VffwMHfzKGabzbC8v7u6vjk/Pz89Ozs5OV2cXyyurpf3q1JqtSDhu9/4Lh6Nvv/1MPfwaha+ExEeAIgJQHgAAZAIbxEITFsRgR3aElbRLSKpZuZWay2lupubAyAmJgYQCDe3cK+1mCECRMLMRMTCwsLMxGDCL/JSaxnCDAAJC5NISkmJmYgiwqtZeLhHBHaICP8GBICYmAgAgYiJQI6oFuGOnyNiURFJqXz9u3j0xfNv5ZRVlZhBhJ/jIOTcdP1oMpnszfa6vk8pMbGHMxEziwiLuPtqvY6IJjeiAsCqlbpxNzxKok3bdm03Hvd9320BVDab+fnZTz/48OT05Or6+vb29v7uvgyFmYSZiIlJVACYWYS5O3aImFm2NEmTctN2KeemaUb9aDKZ9KNR17YA1ptNLcXdATAJQCBS1b7tUtNo1kCUYQAj5yblJJJVdlSSNjnnJqem68aj8XQ6ns1m43HXNKrK+DQCEEA8f+cUO+8+e/v5tz/EDv3jX923CK9W3SPoh2/9GDtf+tFXzGMoVorVarWaBQBmEk6aUzPqu9lstr9/eHx8fHhwsL+/tzed9n3fbTVdbnOTsySVLGA2t4C7w8FMEM05N03TppyTKkgCMI8ylGHY3Nwsz8/npycnH3/0s/nJ2eXVxWq1smpuBnhYEJxFUkqHx8df+MIXXn/jjdc++3y2t9/mzEIRQYAQzK2UEm6EQGArwmsNc/Nq1d08iplVW63X/+x//4fY+ePD/+bkdH5xcXl9s1yt15tazOPuN/8CO9MffMMDsWXu4dUsfCciPAAQE4DwAAIgYlIRYqYtEDMBICKASIhZU1KAi22VYVOKVTczdwaxMDFjy8PMqlXbwRaRMDMRsbAwg4jpAQif4ma1DBEGgElURVVFlbZAHu7VqlmEhwd2WBggPAj8AgJATExEIADEFA6LCI9AAEQg/t0f4tHBj3+vaducc0ptkqxJtsACINw8EEQ5teNxP55M9/f2un6kqkQUEUSkIkQMpnDfDAMzt22rIu5eStmUtZuBiIkAEuYm56Ztx+PRuO9H4xGByrA5P59/8DcfvHj16nJxcXN7s95sopqIECECzCxJQHDbquaBcBATQUVYRFVzSjl1TZu3ur6fTaf9aNy1LUCbYeNmHsFETAoiAEm1aduUMieBe6kDCCk3KeekyqIiopLyg6bJueu6fjSaTad7e7N+3DWZVRmfRtgJIIB4/s4Zdl5+5wjAf/C1Zxbxvc//EDtffv8tMAHkgHkMxUqxWq1WswDATMJJc2pGfTebzfb3D4+Pjw8PDvb39/am077vu62my21ucpakkgXM5hZwdziYCaI556Zp2pRzUgVJAOZRhjIMm5ub5fn5/PTk5OOPfjY/Obu8ulitVlbNzQAPC4KzSErp8Pj4C1/4wutvvPHaZ5/P9vbbnFkoIggQgrmVUsKNEAhsRXitYW5erbqbRzGzaqv1+vb+7vrq+uxsfjafn5zOLy4ur2+Wq/V6U4t5EPHyN76LR6Pvfz3MPbyahe9EhAcAYgIQHkAAREwqQsy0BWImAEQEEAkxa0oKcLGtMmxKsepm5s4gFiZmbHmYWbVqO9giEmYmIhYWZhAxPQDhU9ysliHCADCJqqiqqNIWyMO9WjWL8PDADgsDhAeBX0AAiImJCASAmMJhEeERCIAIRCJJH6zf/nM8+uXzbyXJmmQLLADCzQNBlFM7HvfjyXR/b6/rR6pKRBFBRCpCxGAK980wMHPbtiri7qWUTVm7GYiYCCBhbnJu2nY8Ho37fjQeEagMm/Pz+Qd/88GLV68uFxc3tzfrzSaqiQgRIsDMkgQEt61qHggHMRFUhEVUNaeUU9e0eavr+9l02o/GXdsCtBk2buYRTMSkIAKQVJu2TSlzEriXOoCQcpNyTqosKiIqKT9ompy7rutHo9l0urc368ddk1mV8WmEnXj+zgl23n329vNvf4gd+kdf3QvzwdzMAfzVV/41dn7l/a+YRzEvpQ62FW7ujgCrSG6avhvt7U0PDw6fPHlyeHR0dHCwN5uN+lHXd01uc1JW0SSUhISxxSAIMxFL0qS5SSmnlJjFQe5hZqXYZjPc3Fyfnc1fvnzx8c9+Nj89ubq6Xq9X4RYWDkdACCKSm+bJkye//CtffOONNz/z/DOz6UxViAkAEZQ4wkoZzA0BBBAeEbWauVm1al7dSrVS6/16/af/xz/Czj8e/Vfz8/nV1c3d3d1qPQxWzWP5G+9iZ/r/swUnzZZm13mY33etvffXnPa2mXWzGhCsBi5AIpoCaSuosAcESVEsDWgrPHEoTIUdYnjsX+CZQz8BQ3vi8BARnjpkyyGLSKIxIQAEitnnzZuZlXn75pxvr7V87qm6rEJQz/PT3/VArJh7eDULX4sIDwAUAggPIABSmFQpIhQRXgOpQqGqiihFw2FehzoMQ10Ogw3VEQKKCkUAhHs1s2GoZgEgAqSKkBQRipAUkEIAJHHDzWwYgCCoIqKacxJVggA83KtVMzcLOABCRJVCrIUH/h4K8QUCDAAeARAi//nPcGPr139QSsm5lNzklFWzJl1xINzMEUAuZTweTyez+Xze972mRNLdRURFRege1S3cRbXvOhGtVodhuVhcuZuoComAiOSUmrYZjUaT8Wg8mWbVxdXVq1cv79+//+Tp/qcvXp6cni6HBSJUlIC5C6EpgTCr5oHwcKxQhURS1ZRzTk3TNaWUpum7bjQaj0ajtuuS6jAMHiFckaQJFESISC5Fk4ISCPcqIqWUXEpKWfRa0pxLaXJZadtuMp1Mp9P5bD4et7mIKkl8gbgRgO99/AJr+z/YBvDxN26F+Y/e/QXWPvj5+wIBxADzGMyHoS5tJdzcHQFJqqVp+m40n0+3Nrd2d3e3tre3Nzfns9moH3V915S25CRJU1ZmpQpWBISKkKI55VSanEvOWUQddA8zGwZbLJYnJ8cvXrzc33/65NGjl88Pjo6Or64uwy0sHI6AEqpammZ3d/fd9997552vvLH3xmw6S0kpBEAiUSJsGJbmhgACCI+IWs3crFo1r25DtaHWi6urk9PT168PX7z49MWLly8/fXl0dHJ+fn55tVxaNQ9Qzr75l7gx+vF3w9zDq1n4WkR4AKAQQHgAAZDCpEoRoYjwGkgVClVVRCkaDvM61GEY6nIYbKiOEFBUKAIg3KuZDUM1CwARIFWEpIhQhKSAFAIgiRtuZsMABEEVEdWck6gSBODhXq2auVnAARAiqhRiLTzw91CILxBgAPAIgBBVTTnnki++9e9w492X/3VOWTVr0hUHws0cAeRSxuPxdDKbz+d932tKJN1dRFRUhO5R3cJdVPuuE9FqdRiWi8WVu4mqkAiISE6paZvRaDQZj8aTaVZdXF29evXy/v37T57uf/ri5cnp6XJYIEJFCZi7EJoSCLNqHggPxwpVSCRVTTnn1DRdU0ppmr7rRqPxaDRquy6pDsPgEcIVSZpAQYSI5FI0KSiBcK8iUkrJpaSURa8lzbmUJpeVtu0m08l0Op3P5uNxm4uoksQXiM/sffwMa3dvf7T3/YdAAOA/+do0Iupg1d2BX3/jb7H27s8/DI/BfLC6HMLM3NwcAYhoKc24H82m863trVu7O9s7O7e2t2fz+WQ8btuuKU1KSqEmESWSUFSTqiRVlaSiOWtJKWnKEHiwWrhZrcMw1OPjk+fPXzx9+vjxw4fPDw6Oj08uLy/CDR4eLgBFSkpN196+dfv99z946yvv3Lp9ezwZq1ApAChIoggb6mBmiEAAAXNzs2orbhHuPlRbWr24uPzv/7c/wdofl3/18tPXx8cnlxeXl8PSqtWI82/dxdr0p7/rAXcPdzN3M3e38PDAGoUAwgNrFCZVipBUEa6IJBVR1aRC9YB7mNtQh2Gow1DNqrkLSKFQAASimtkwVLMAEAFSRbgCUJTCFQEBUIgb4e5mcBeBiIpq1kQVFXEPD7dazdzNsEahqAqJL/EI3AgPfC5wjVQFGOZYEZF//DPc2PnkeynnrCXnrFpyVklJVUExM18BmtyOxv10Op3NN/quTymBRATInBKA6h7uADSlrutUdVhZLC6XV+GmqiKCAEkRaZoy6keT6Xg6nWZNw3J5ePjq/oMHTx49Pnh+cHJyWusAh6oCMDPAmVQI80B4OD5DASgqVE255KbpmtI2benbrhuNx6O+63tVNTMAIqKykglGOABNSUQcAAFESlpKk0pOmlRVRDVpzk2TSy6l67rpZDqbTefzjfG4y4WqBEDic8SNAHzv4xdY2//BNoA/+U92IuKn7/0Sa+/97LcJhYhDwmMwH6wuhzAzNzdHACJaSjPuR7PpfGt769buzvbOzq3t7dl8PhmP27ZrSpOSUqhJRIkkFNWkKklVJalozlpSSpoyBB6sFm5W6zAM9fj45PnzF0+fPn788OHzg4Pj45PLy4twg4eHC0CRklLTtbdv3X7//Q/e+so7t27fHk/GKlQKAAqSKMKGOpgZIhBAwNzcrNqKW4S7D9WWVi8uLo+Pj19++mr/4PnLFy9efvr6+Pjk8uLyclhatRpB8uybP8SN/kcfhbuZu5m7W3h4YI1CAOGBNQqTKkVIqghXRJKKqGpSoXrAPcxtqMMw1GGoZtXcBaRQKAACUc1sGKpZAIgAqSJcAShK4YqAACjEjXB3M7iLQERFNWuiioq4h4dbrWbuZlijUFSFxJd4BG6EBz4XuEaqAgxzrIgkTSnnXNL57/w73Pjt5/9cteSskpKqgmJmvgI0uR2N++l0Optv9F2fUgKJCJA5JQDVPdwBaEpd16nqsLJYXC6vwk1VRQQBkiLSNGXUjybT8XQ6zZqG5fLw8NX9Bw+ePHp88Pzg5OS01gEOVQVgZoAzqRDmgfBwfIYCUFSomnLJTdM1pW3a0rddNxqPR33X96pqZgBERGUlE4xwAJqSiDgAAoiUtJQmlZw0qaqIatKcmyaXXErXddPJdDabzucb43GXC1UJgMTniM/sffwMa3dvf7T3/YdAAOAfvjfx8FrNwsLxt7/zAGvv/uxrDlTzYbClhZvbiiMQlFRyMx6NZtPZ1tbm7Vu3dle2dzY3NyaTSdt1TSqiCoEIKGBSySnnrKkkVU0qTKpZRCEEJQLV3c3MI9xOT8+ePTt4+vjxw4cPDp4dHB2+vri4cq9h1QMCaJKmKX0/uv3G7Xff/eDtd97a2d3pRyMhAQiFgqQKoNZlmDNWgEB1q9XCLYAgAJrFYMPp+cV/+7/8Ada+l/7Vy5cvj49PLi6uFsNycI+Ii2//FdZGP/5uRMCjmnm4V7Pw8AACaxQR0iOwJiRBEYFwRUVENWvSnHLJAIeluVsNWxmqWV0zAyAghSsRYe5ua+6IAElAVQlQhCIkAQiI3xAwJ4LCFV0RFVWsuZm5rUQEAJIAKRSSIH5TIAB4RHgAcc2DKqJJIBYOB0TkH/81bux88j1NWlKTUtGUkyZdyUqymkWEO0tT+tFoMpnMZ/Ou7XPJIBEBUlVJujvWcspd36nqYrlcrCwvw11VheIRAoBsSulH/XQyns5mOSWv9fDo8NGjR0+ePHn65Mnx8XEdDHBCAJiZw1WFIG5Q8HcoklRTKl3fdW3XtF3fdW3XjdZyStUMgKxQVBTgUCsiRASkR4BU1dLkXErOWTSpUFZUcyo5l6aUvu/Hk+l8Nt3Y2BiP+9KoCEXwOYIAiLUAfO/jF1jb/8E2gD96f9vD/8PXfoW13/7pVyBKKiAOVPNhsKWFm9uKIxCUVHIzHo1m09nW1ubtW7d2V7Z3Njc3JpNJ23VNKqIKgQgoYFLJKeesqSRVTSpMqllEIQQlAtXdzcwj3E5Pz549O3j6+PHDhw8Onh0cHb6+uLhyr2HVAwJokqYpfT+6/cbtd9/94O133trZ3elHIyEBCIWCpAqg1mWYM1aAQHWr1cItgCAAmsVgw+n5xavXRy9evNzf3z94/uLly5fHxycXF1eLYTm4RwTAi2/fxY3+7neqmYd7NQsPDyCwRhEhPQJrQhIUEQhXVERUsybNKZcMcFiau9WwlaGa1TUzAAJSuBIR5u625o4IkARUlQBFKEISgID4DQFzIihc0RVRUcWam5nbSkQAIAmQQiEJ4jcFAoBHhAcQ1zyoIpoEYuFwQCSpllJSzme/8//gxlcP/rmmnDTpSlaS1Swi3Fma0o9Gk8lkPpt3bZ9LBokIkKpK0t2xllPu+k5VF8vlYmV5Ge6qKhSPEABkU0o/6qeT8XQ2yyl5rYdHh48ePXry5MnTJ0+Oj4/rYIATAsDMHK4qBHGDgr9DkaSaUun6rmu7pu36rmu7brSWU6pmAGSFoqIAh1oRISIgPQKkqpYm51JyzqJJhbKimlPJuTSl9H0/nkzns+nGxsZ43JdGRSiCzxEEQKzsffwMa3dvf7T3/YdAAOAfvDdx8+rVzT3i/jcfYu2rP/uaBdx8qDZYuLmtOCKC1JKbftRPp9Otzc3d3Vu7uzu3dne3Njen02nf900uIioCCChg0lxSyiXnrJpEFVCKCJXCgDgQAb8WEX5yenqwv//48eMH9x8cPHt2eHh0eXERdi3CSZScu64dTSZvvLH37nvvvnnnza2dna7viM8lZUpKoNZq5oJAwAF3r7VGOCggAaluy8Xy5Ozsz//XP8Daf8H/7tOXn56cnl1dXi2GZfUI84uP/gprox9/N1bczdzN3N3CwwMIXCOFQnoE1oQkSCEAigglqeacS1NSSQEsrpbDYA64WzWzes3NHQFAQKw5ws3c3cwCICAiXAEoApDCFQAC4gvBcAIkASZVWVEFEIgwNzczBwLXSCHWhARAkELcCI9AmDkQ1zyokjRTpJoBIIW//9e4sfvJ9yRp1pxS0ZRzSqI5JaXQPCIQQEqp7brxeDqbTUf9KOcsIh4hpKiSDHwu59z3vVCGYbm4WiyWlx4uqkLCwyMANCV3fT8ej2aTaVOKhZ2dnO7vP33y5MnDB48Oj14PwxAWBANRzQCnCkERkEKQgi9QkmrOpeu7ruv7rmu7ru/6a6NR0uRuAEQEEBV1D7PqEUKCsAiKpJSaUnJTcs6iSQiSojmnlHNpSum6bjqdzmazzc3N0bhvGxUVEXyBILEWgO99/AJr+z/YBvC997fd/Bcf/gpr7/zkK6CIKKkWcPOh2mDh5rbiiAhSS276UT+dTrc2N3d3b+3u7tza3d3a3JxOp33fN7mIqAggoIBJc0kpl5yzahJVQCkiVAoD4kAE/FpE+Mnp6cH+/uPHjx/cf3Dw7Nnh4dHlxUXYtQgnUXLuunY0mbzxxt6777375p03t3Z2ur4jPpeUKSmBWquZCwIBB9y91hrhoIAEpLotF8uTs7OXr16/eP7iyf7BwfODT19+enJ6dnV5tRiW1SPMIbz49l3c6O5+28zdzN0tPDyAwDVSKKRHYE1IghQCoIhQkmrOuTQllRTA4mo5DOaAu1Uzq9fc3BEABMSaI9zM3c0sAAIiwhWAIgApXAEgIL4QDCdAEmBSlRVVAIEIc3MzcyBwjRRiTUgABCnEjfAIhJkDcc2DKkkzRaoZAFKSpFJyzuXkd/4tbvzWs/9SU84pieaUlELziEAAKaW268bj6Ww2HfWjnLOIeISQokoy8Lmcc9/3QhmG5eJqsVheerioCgkPjwDQlNz1/Xg8mk2mTSkWdnZyur//9MmTJw8fPDo8ej0MQ1gQDEQ1A5wqBEVACkEKvkBJqjmXru+6ru+7ru26vuuvjUZJk7sBEBFAVNQ9zKpHCAnCIiiSUmpKyU3JOYsmIUiK5pxSzqUppeu66XQ6m802NzdH475tVFRE8AWCxMrex8+wdvf2R3vffwgEWNJxSwAAIABJREFUAH7vvamFD8NgK+6Pvv0Ya+/89QfhtIhafajm1T3C3N1AlaypbbvJZLK5sbG9vb2zu3N7d3dra2s+n49H47Y0qSQCkkSUTJJyyVlTKqoKEhQESYEoQYiADMAs3O34+Hj/6dPHjx7fv//gYH//5OTo4uLCLTwM7qLSlDIejSaz2RtvvPFbX/3tO2++ubm12XddhBPXRKUkBWAr7hK4RriHmbkDggDDY7kczq4uj09O/4f//U+w9vv+Lw9fvz49O18slsthOVRz94tv38Xa6MffjQh4eHg1C1+LCA8AFAoJwCNwQykAKCRIlawp51yaUppi5pdXi2EYArAIt7VqK44AICAAR1zzCDePcHcAIsIVgCIAKQRAEl8iAMMJcEVEKZJUKAACEeaD1fDAmqoAMHMgAFIopIgAIIg1dw+ER4QHEICknAlWtwiQwt///3Bj95PvSdKkWTXnVDRpWsmZolizgKo2pYxG4+l02vejXIqqApAbILGWU+77TkQWy+WwXFwurhAuqiTDfAVASqnv2n7UT6fTphQAF+dnL1++3H/69N69e59++mqxWNRaEYC7h4MQEQhIUSEpIsQKBeGgJNWcy6jvu27U9t1K33Vdfy1pcjeskEIBBBFDrRGRVEE6QlZUc85NUzRnFVUVkqKateScSyld102ms435bGNjYzwZNY2qCv6OQIgVEkAAsffxc6zt/2AbwB++v2Phv/ja32DtrR+9QxKilBROi6jVh2pe3SPM3Q1UyZratptMJpsbG9vb2zu7O7d3d7e2tubz+Xg0bkuTSiIgSUTJJCmXnDWloqogQUGQFIgShAjIAMzC3Y6Pj/efPn386PH9+w8O9vdPTo4uLi7cwsPgLipNKePRaDKbvfHGG7/11d++8+abm1ubfddFOHFNVEpSALbiLoFrhHuYmTsgCDA8lsvh7Ory+OT0009fPX/xcv/g4OXLTw9fvz49O18slsthOVRzd5Ln3/ohbvR3v+Ph1Sx8LSI8AFAoJACPwA2lAKCQIFWyppxzaUppiplfXi2GYQjAItzWqq04AoCAABxxzSPcPMLdAYgIVwCKAKQQAEl8iQAMJ8AVEaVIUqEACESYD1bDA2uqAsDMgQBIoZAiAoAg1tw9EB4RHkAAknImWN0iQEpOqZSSczr+B/8WN97Z/7OciiZNKzlTFGsWUNWmlNFoPJ1O+36US1FVAHIDJNZyyn3fichiuRyWi8vFFcJFlWSYrwBIKfVd24/66XTalALg4vzs5cuX+0+f3rt379NPXy0Wi1orAnD3cBAiAgEpKiRFhFihIByUpJpzGfV9143avlvpu67rryVN7oYVUiiAIGKoNSKSKkhHyIpqzrlpiuasoqpCUlSzlpxzKaXrusl0tjGfbWxsjCejplFVwd8RCLFCYu/jZ1i7e/ujve8/BAIA//CDDXdbDMuhmld7/NFjrL390w8ADuFebajh1WtEOCJAMqm2bTfq+/l8trW1tbOzc3t3d2t7e2tzczyZ9E2XS1IVCimiWTWrJk2aKApKYIWgKJWqFAUBsFoMdTg+PNp/8uTho0f3799/cXBwdHS4uLpyCw8PmKp2TTsejeYb89t7e1/96rt37uzNNze7to1wRJAUkZyVQK3m7gKQACWAaoZAANV9qPXy8urs7OLw6Oh//D/+K6z9Z/XPj44Oz88vF1dXy6EOdYiI82/9EGvjn/xurJh7uLmHeSDCIxD4Eo8IDwAUCkmQQooIJanmnFOTcinhcX5+uRxqAIEI82rmbm7uCAACAnDENY9w8wiskQRAgCK4RgpX8CWCYAQBrogkVYoIRYTmHubm5hEAhBSR8KhmQACkUEiCFBLEjUCERyA8QiiaMkgbqgMCid//KW7sfvI9SZqYRfNKymktU1WEgLg7RHJKTdtNJ9O+75u2LTmTlDWKiCqAiMgptV0nFLO6WCyXw1WEqyYBzK8hQpM2Ten7fjKelKYk4WKxODo8era//+tPfv38xcvLi/PlcmnmcFwTKEEVEKSoUKgUfIYiSbWUpmtH/ahbadqua9t+ZTRKmtzNI4QECNA9rFaPSCmRDJJCoeSSmqZJpSRNKSkoSVdKzqXk1Pej6XQym882NzZG475pVFXwJSIAQQIIIPY+fo61/R9sA/ijr912t//wwS+wdufuWxSlKJkADuFebajh1WtEOCJAMqm2bTfq+/l8trW1tbOzc3t3d2t7e2tzczyZ9E2XS1IVCimiWTWrJk2aKApKYIWgKJWqFAUBsFoMdTg+PNp/8uTho0f3799/cXBwdHS4uLpyCw8PmKp2TTsejeYb89t7e1/96rt37uzNNze7to1wRJAUkZyVQK3m7gKQACWAaoZAANV9qPXy8urs7OLw6Ojlq1cvX756/unL16+Pjo4Oz88vF1dXy6EOdYgIkmff/Evc6O9+x8PNPcwDER6BwJd4RHgAoFBIghRSRChJNeecmpRLCY/z88vlUAMIRJhXM3dzc0cAEBCAI655hJtHYI0kAAIUwTVSuIIvEQQjCHBFJKlSRCgiNPcwNzePACCkiIRHNQMCIIVCEqSQIG4EIjwC4RFC0ZRB2lAdEIjmVFZyPvrG/4Ubbz/5s5xzymktU1WEgLg7RHJKTdtNJ9O+75u2LTmTlDWKiCqAiMgptV0nFLO6WCyXw1WEqyYBzK8hQpM2Ten7fjKelKYk4WKxODo8era//+tPfv38xcvLi/PlcmnmcFwTKEEVEKSoUKgUfIYiSbWUpmtH/ahbadqua9t+ZTRKmtzNI4QECNA9rFaPSCmRDJJCoeSSmqZJpSRNKSkoSVdKzqXk1Pej6XQym882NzZG475pVFXwJSIAQWLv42dYu3v7o73vPwQCAP/Jh9vVbDEMdRiq2eNvP8Ta2z99P8DqYWbD0tyjmgdERJWUlJpSRm03mc62tja3t7du7e7ubO9sbW/NZrNR1zWliIqqghCFZF0RUVBBAAoCVNUkolABCcCrLRbLw9dHj588fvjwwYN79w+eH5yenFxdXcU1j0BWaZpmPJ7MN2Z7d+68++57e3t3Nrc226Zxt4ggKSI5K4HlMCACpFJAAIwId5jbchgurhZn52dHRyeHR0f/0//9L7H2e4t/cXJ6dn5+sVguloul2WAe59/6IdbGP/nd+Iy7mXs4PAKBtfAIhEeEBxC4RlVZoYhQRCiqWZPklLNWi6uLy+VQAThixdzdLDywRiGA8HCEm4V74AvENYrgGinEGkl8JkLcARdRUnJOoqoiJM3cw71aIAAQFJFAVLNwB0ihkAQpBEAQNwKBNUIkJ4A2VPegiP+jn+DGrXt/RGFiFs0rKacV0bWUAZgh4IQ0bRmNJv1oNOr7UoroNfmMKoCISKJd36mqmdU6LJbLgGdNANzcwwEklZxTU5p+PG6a0miqXi/OLw4Onv3qV7/af7p/enZ6eXFVbYCBKiqkghSsEEIVgVApAEWFqimXpuu6vuuatmvbpmnaruvG43FKyd0jAp8JukddDgAkqYiAhHCl5Ny0bSlNLk1KKqpZk2rOpZRc+r4bTybz2Wxjcz4edbmoKLFGXBMBCBJAALH38XOs7f9gB8CffH2vmv3svZ9h7c7ddygklaIBVg8zG5bmHtU8ICKqpKTUlDJqu8l0trW1ub29dWt3d2d7Z2t7azabjbquKUVUVBWEKCTrioiCCgJQEKCqJhGFCkgAXm2xWB6+Pnr85PHDhw8e3Lt/8Pzg9OTk6uoqrnkEskrTNOPxZL4x27tz591339vbu7O5tdk2jbtFBEkRyVkJLIcBESCVAgJgRLjD3JbDcHG1ODs/Ozo6OTw6evXq8NXh4evD45WT07Pz84vFcrFcLM0G8yB59s2/xI3+Rx+Fu5l7ODwCgbXwCIRHhAcQuEZVWaGIUEQoqlmT5JSzVouri8vlUAE4YsXc3Sw8sEYhgPBwhJuFe+ALxDWK4BopxBpJfCZC3AEXUVJyTqKqIiTN3MO9WiAAEBSRQFSzcAdIoZAEKQRAEDcCgTVCJCeANlT3oIhqKk0upRx++G9w4+0nf5ZzTjmtiK6lDMAMASekactoNOlHo1Hfl1JEr8lnVAFERBLt+k5VzazWYbFcBjxrAuDmHg4gqeScmtL043HTlEZT9XpxfnFw8OxXv/rV/tP907PTy4uragMMVFEhFaRghRCqCIRKASgqVE25NF3X9V3XtF3bNk3Tdl03Ho9TSu4eEfhM0D3qcgAgSUUEJIQrJeembUtpcmlSUlHNmlRzLqXk0vfdeDKZz2Ybm/PxqMtFRYk14poIQJDY+/gZ1u7e/mjv+w+xxn/6D24PQ13UZR2WQ7XH33qAtbd+8n6ANcLNhqWZRzUHIJqTKEVKKaO2m0zG842Nna3tW7d2d1a2t2ez2bjvm6ZIUhUVhahSqSqkkhoERUFVUeo1kKB4uFdbDvXw9eHDR48ePnhw794nzw8OTk5PF1cLhEcg3ES1yWU8Gs03N+68eef9Dz648+ZbmxsbTVPM3COEoDCnDLjV6u4iAlJFAJqHu1ez5dXy4ury5PT09eHx4dHx//zDv8Da7y3+xcnp2fn5xdXV5XK5HGqNiItv38Xa6MffjQh4eLi5RwQ8AoEb4WHh4R4RAEhRFRUVXRGCVFGhqIqou11cLW1ZQ2AegQh3N8cahSsAIsLc3czd8SXENYoABEAh1kjiM+FRjYCKiGrOSVSTKkEPN3c3c3cABCUpAKvVIwAICYAgAAoBEMRvomrSBHCwGuaA2D/6MW7cuv/HJBKz5FxSyTmJKiCSNKtCaBbVV6Lk1Hfj8WQ8Go3attV0TWUtJQDurqpd1yVNHu5uQx2AUFUhvXogAKhQVJpcuq5ru6YpBcCwXB4cHPzil798+uTJ4dHxxdl5XQ6BENWkpAiIzxFCFYFQqZJUNWkppW27ru2aa23TNH3fj8bjnLO7RwSA8IhArTYslx6RVCECQFQokkvuuq5pu6Y0OZekqinnXEouOaWu7yaT6Xw+nc3n43GXs4gSa8Q1EYBYIQHE3scHWNv/wQ6AP/2Hbw9D/et3f4q1O3e/QhFwRQOsEW42LM08qjkA0ZxEKVJKGbXdZDKeb2zsbG3furW7s7K9PZvNxn3fNEWSqqgoRJVKVSGV1CAoCqqKUq+BBMXDvdpyqIevDx8+evTwwYN79z55fnBwcnq6uFogPALhJqpNLuPRaL65cefNO+9/8MGdN9/a3NhommLmHiEEhTllwK1WdxcRkCoC0DzcvZotr5YXV5cnp6evD48Pj45fHR4dHx8fHZ+cnJ6enJ6dn19cXV0ul8uh1oggef6tH+JGf/c7Hm7uEQGPQOBGeFh4uEcEAFJURUVFV4QgVVQoqiLqbhdXS1vWEJhHIMLdzbFG4QqAiDB3N3N3fAlxjSIAAVCINZL4THhUI6AioppzEtWkStDDzd3N3B0AQUkKwGr1CABCAiAIgEIABPGbqJo0ARyshjkgmlMpJed0+OG/wY239/+spJJzElVAJGlWhdAsqq9EyanvxuPJeDQatW2r6ZrKWkoA3F1Vu65Lmjzc3YY6AKGqQnr1QABQoag0uXRd13ZNUwqAYbk8ODj4xS9/+fTJk8Oj44uz87ocAiGqSUkREJ8jhCoCoVIlqWrSUkrbdl3bNdfapmn6vh+Nxzlnd48IAOERgVptWC49IqlCBICoUCSX3HVd03ZNaXIuSVVTzrmUXHJKXd9NJtP5fDqbz8fjLmcRJdaIayIAsXLnnz3D2t3bH+19/yHW+E+/ecdrXSyWy2EYan30zb/F2ps/+cAA93CzOsTg7ma4JiKaVEvObdtNJuON2cb2zvat3Z1bu7tbm1uz+WwyGpWmpKRJVbNqUlEVISkiSiFFqVlVqUlEAHqEu5uHOw4PDx8+fHj//r17n3zy7ODg9OTk6uoKCF+xJSE5pb4fzWbTO2+//eGHH7711jubG/PStOYWYQCSUDQDbrWauapQVIQArEY1c7fFcri4XJydnb46Oj46PvnXP/wLrP3e8Ocnx8enp+dn52fLq0XAAVx+9COs9Xe/EwgzD0SYByI8AgGAIIBAeISbhQcAUdGVlJKqqHJNCEKgrGbLq8FWEOERCDd3BAAV4QoIwMOrmdfqEVzBFyiCa6QQN0hiLczDK8KTrqScs2pKqhDCo5q5mbsDoJAiJN0sPPAfQyEAglgRAhBqyhnAMNQIB6T+pz/CjVv3/5hE0qyacyo5FxFUdxEppYBcVquDmVXRNOpH4/F4Mpl0XVdKSSnJimpOCaSZqWrXdTlnAO5uViHIKYGEOT5DiiBrarq2b9umbbNqNXvx4vnPf/6Lxw8fvXr96uzkdLlcAlBNKQsIEAhEOCkghJpUJUlSTankUpqmaUvbtKVpmrbt+r4fTyYlZ49AhEe4u1UflsNisTAzEQERAEVSzk3TdKO+67q26ZrSpJRyLjmXnHPKpe+6yWQ8m802NuZ93+VMUYJYIW4IhFghsffxM6zt/2AHwJ9+67e81p985UdYe/NHXw0QFFIMcA83q0MM7m6GayKiSbXk3LbdZDLemG1s72zf2t25tbu7tbk1m88mo1FpSkqaVDWrJhVVEZIiohRSlJpVlZpEBKBHuLt5uOPw8PDhw4f379+798knzw4OTk9Orq6ugPAVWxKSU+r70Ww2vfP22x9++OFbb72zuTEvTWtuEQYgCUUz4FarmasKRUUIwGpUM3dbLIeLy8XZ2emro+Oj45Pjo+Pj49PD05Pjk9OT4+PT0/Oz87Pl1SLgAAhefPRXuFH+/TcDEeaBCI9AACAIIBAe4WbhAUBUdCWlpCqqXBOCECir2fJqsBVEeATCzR0BQEW4AgLw8GrmtXoEV/AFiuAaKcQNklgL8/CK8KQrKeesmpIqhPCoZm7m7gAopAhJNwsP/MdQCIAgVoQAhJpyBjAMNcIBSSmVXDTnww//T9x4Z//Pcio5FxFUdxEppYBcVquDmVXRNOpH4/F4Mpl0XVdKSSnJimpOCaSZqWrXdTlnAO5uViHIKYGEOT5DiiBrarq2b9umbbNqNXvx4vnPf/6Lxw8fvXr96uzkdLlcAlBNKQsIEAhEOCkghJpUJUlSTankUpqmaUvbtKVpmrbt+r4fTyYlZ49AhEe4u1UflsNisTAzEQERAEVSzk3TdKO+67q26ZrSpJRyLjmXnHPKpe+6yWQ8m802NuZ93+VMUYJYIW4IhLjzz55h7e7tj/a+/wgIAPzTb79lQ11cLRbDchjq42/9Ldbe+PEHAbhFNauDVQs3dyCpUDRRS85t245H/Xy+sbG1cWtnZ3t7Z2d7az6bzybjru9yyVlVsn6GSqWKCEQhVM1ClawQBeixAnOPwMnJ6f7Tp/fu3/vV3/zNkydPTo6PLi6vwi3MPIyAqI76bjbffPudt77+9W+8+eZbs/msa9rANcJBSUKPGOrgHgQoVM0IDNXczdzrUC+vlqdn54fHx6+Pjv71D/8Ca9+++G9OTs5WLs7Pl8MSEUBcfffHWCv//puBWIFHIMIjEB4BQEiseYSbhQcAUdEVUVFNqlRRIQAPeHg1t2pe3RCBuObhCAAqwhUQgIdXM6818BuIaxQBSCH+nnCHDXCIas4padKcsiYI4eHh1cyrBYKgiAAIBNbCIxD4TQQppAgAkioqKQGo1dyD4PB7f4Ubt+79EcgkSTXl3KgmVVoEqSkrwGGwarVWU9W26yej8Ww+6/u+bduUs6ypKkhEiGrTNDklAB4e7hDklEAiIABIXIusWtqma9uubZKqebx48fyXv/jFo0ePXr9+fXp6tlws4EhZhQJBwLFGCkWSqqjmpJpy1pSb0pSSS1Nybpqmbdu+H40nk5ILroW7VzMbfBiGxXIZ7pQVQoSiKaWmKW3XNW2bcymp5JJzLk1pcik5567rptPZbDqZz+d93+ZMUWKFIG4IhFghsffxM6zt/2AXwMff+aoN9Sfv3MXaG3e/AgiEYArALapZHaxauLkDSYWiiVpybtt2POrn842NrY1bOzvb2zs721vz2Xw2GXd9l0vOqpL1M1QqVUQgCqFqFqpkhShAjxWYewROTk73nz69d//er/7mb548eXJyfHRxeRVuYeZhBER11Hez+ebb77z19a9/480335rNZ13TBq4RDkoSesRQB/cgQKFqRmCo5m7mXod6ebU8PTs/PD5+fXR0eHh0dHR8eHx8eHxycnK2cnF+vhyWiAACwNV3f4wb6f/9h/AIRHgEwiMACIk1j3Cz8AAgKroiKqpJlSoqBOABD6/mVs2rGyIQ1zwcAUBFuAIC8PBq5rUGfgNxjSIAKcTfE+6wAQ5RzTklTZpT1gQhPDy8mnm1QBAUEQCBwFp4BAK/iSCFFAFAUkUlJQC1mvv/Txfc9uh5nndi//+Ph/O6rvthhpzhw5CyrQcnK8mRk8ihk3ctsEi22TR2Ui/yGYq+6ot2t5+hQFEUXRQLOJ8iydtFil0sAsfSynZiKXogLVGUTHLIIYcczsw913Wex9F7bpuNDNu/XxI0Vfeiro+/9p/w3Fc++3P3TtVU2TJJNVeA09Rqq7U2Ve2H2XK+2L6wPZvN+r43d9lQVZDIFNWu69wMQGRkBARuBhIJAUDiXLpq6buh74e+M9UWub9///1/+qdPP/300aNHR0fPxrMzBMxVKBAkAhukUMRURdVN1dzVvCtdKV664t51Xd/3s9l8sVwWLziXEVFba1NM03Q2jhlBWSNEKGpmXVf6Yej63r0UK17cvXSl81LcfRiGra3t7a3lhQsXZrPenaLEGkE8JxDihW/fxcbbezeu/+UdIAHw27/3lWmsJ6vV2ersbBzvffMTbFx9518AqC3b1MZpqi1bA0lfU1VRd+uKD7PZcrm8cOHCpZ2dS7s7u5cu7V7c2d25sFwu57PBXCOhAjUTdVWlEERSAKWQqiIqqqAAyECt7eT0+NHjw9u3P/6HH/3Dxx9//PjR4+Pj42g1IyggQch8Mbt06cqLL33la6+/fu3aC8vlYhgGd6cQmZIAERFTra21iAZSqAmJaC2x1mo7G8dnz04PDh8fHDz639/6H7Hx6oPvHD87Pj1dTdNYoyEjI05vvIMN+95vIzKRADISQCKxEZlCEkxkawEkQAqFJEihqLq7rBG1xTiNrbUIAkhkArkWiV8lkOciM1pk4jmuARQBSOFaZgLISCAzgtkAkOJ2Ts3cjCKIjIw61RattcAGhUrBRiIjE0BGAqAQgJAiQhGugaoqqqBEbYkEuLrxFp7b/egPhRRRU1d1MxVzVROlQGq2OrUamRFmNgzzxWKxtbW9WM5nw1BKoYiQWJNzquruQkZmRkQ2EKoqFBEKhSCAzKYqXd93Xdd3RSitTvf39z/68KPPP/v88eHh8fFxHc9aJEEIMhoEpJiqiJqquaqaqpmreylupfTmxU3dy9psNlssFl3XqyqQ0zTV2lqLiMwIguYmZl5cVQExUy+dqEZAVbpSvPRdV7pSzMtsNmxvb1+8cGG53JrNOneKEsTPEOdEAILE2vVv3cXGT//mKoA/u/HKNNYfvvgWNq78/ZcJgSjUANSWbWrjNNWWrYGkr6mqqLt1xYfZbLlcXrhw4dLOzqXdnd1Ll3Yv7uzuXFgul/PZYK6RUIGaibqqUggiKYBSSFURFVVQAGSg1nZyevzo8eHt2x//w4/+4eOPP3786PHx8XG0mhEUkCBkvphdunTlxZe+8rXXX7927YXlcjEMg7tTiExJgIiIqdbWWkQDKdSERLSWWGu1nY3js2enB4ePDw4e7T94uP/w4cODR4dPj46fHZ+erqZprNGQkREATm+8g+f0794AkJEAEomNyBSSYCJbCyABUigkQQpF1d1ljagtxmlsrUUQQCITyLVI/CqBPBeZ0SITz3ENoAhACtcyE0BGApkRzAaAFLdzauZmFEFkZNSptmitBTYoVAo2EhmZADISAIUAhBQRinANVFVRBSVqSyRAERVVUz184z/juRduf0vVzVTMVU2UAqnZ6tRqZEaY2TDMF4vF1tb2YjmfDUMphSJCYk3Oqaq7CxmZGRHZQKiqUEQoFIIAMpuqdH3fdV3fFaG0Ot3f3//ow48+/+zzx4eHx8fHdTxrkQQhyGgQkGKqImqq5qpqqmau7qW4ldKbFzd1L2uz2WyxWHRdr6pATtNUa2stIjIjCJqbmHlxVQXETL10ohoBVelK8dJ3XelKMS+z2bC9vX3xwoXlcms269wpShA/Q5wTAYgXvn0XG2/v3bj+l3ewwf/+d7881Xq6Wo2rs7Oz8f4f3MbGlbd/M8F2Lqep1RYRWDNXERWqmxTzWd8vlsvtC1uXLu5cunzp8uXLl3Z3d3Yubi+Xs/lQ3ACoiLlR1VRBCSCATIJCPSdiokIygVbb2Xj27OjZ7U8/feedH9y6dfPhg4dHR09rrYhQFVESWCwXV65cfemll1597dUXrl2fzeezYfBSTBURmcjMiDbVqbUarSVIVVAykBkBtJpn4/j02fHjw8P9hwf/x9v/EzZe+exPjo9PV+NYW8sIIIE8+cbb2LDv/TYiEwkgIwEkEhuRKSRBAC0jIykUEs+pWSnFVADUqZ2OqzY1iIBMIDMBZCSAQOJXycyoNTIjAhsiQlJEAFK4lmuRQGZEZjKCAlJM1IurmZtRBEBGTLW2WltrmQmApKpiIzIzEucS50ihkCJCEa6BqiqqoGRERgI8vfF9PLfzwb9UVVE1cVWn0M3ViygBtJattUhEppt1/bCYL7a2lovFYjablVIoIiTWRMxMKaICIFoEIjNA6JqIqYooQZwLUy1915fixQlM07S/v3/zo48++/zzp0+eHB+f1umstSSYaJkJQlRN1d3V1FTV3NXE1N3cStf17qbm7la8DMOwWCy6rne3iJzqVKfaWiITpKqaqrtb16kpkqLq7qDUqQIspXR913d91xVTmy3mOxd3tre3FovlbNa5U4QgQKwR50RAAsTa9W8hFUxsAAAgAElEQVTdxcZP/+YqgD998+Wp1n986W1sXP67L0NAKtQTbOdymlptEYE1cxVRobpJMZ/1/WK53L6wdenizqXLly5fvnxpd3dn5+L2cjmbD8UNgIqYG1VNFZQAAsgkKNRzIiYqJBNotZ2NZ8+Ont3+9NN33vnBrVs3Hz54eHT0tNaKCFURJYHFcnHlytWXXnrp1ddefeHa9dl8PhsGL8VUEZGJzIxoU51aq9FaglQFJQOZEUCreTaOT58dPz483H94cP/e/fv7Dx4+PDh8+vT4+HQ1jrW1jAASSESe3PiveE7/7g0AGQkgkdiITCEJAmgZGUmhkHhOzUoppgKgTu10XLWpQQRkApkJICMBBBK/SmZGrZEZEdgQEZIiApDCtVyLBDIjMpMRFJBiol5czdyMIgAyYqq11dpay0wAJFUVG5GZkTiXOEcKhRQRinANVFVRBSUjMhIgRUTPPXnjP+O5F25/S9UpdHP1IkoArWVrLRKR6WZdPyzmi62t5WKxmM1mpRSKCIk1ETNTiqgAiBaByAwQuiZiqiJKEOfCVEvf9aV4cQLTNO3v79/86KPPPv/86ZMnx8endTprLQkmWmaCEFVTdXc1NVU1dzUxdTe30nW9u6m5uxUvwzAsFouu690tIqc61am2lsgEqaqm6u7WdWqKpKi6Oyh1qgBLKV3f9V3fdcXUZov5zsWd7e2txWI5m3XuFCEIEGvEORGQuP7tu9h4e+/G9b+8gw3+4deuRmvj2TTWaZym+9/8BBu7f//VhCSiNbTIzMhAy4waOCcmLK7DMCyXy52LF69cvrx3de3ypZ3dCxe2Z/NZX7yU0nXFzcRVVUgFWSMz0HBOVEWM5iqkCECITOP47PjZ7U8//dE7P/jo5s39+/efPn1axzERFDEVIRdbi2t711588cXXX3t1b+/aYjbvhsHdVCRjLZFo0VqbaqvZkICYkgQkEzViqvV0tTo6enbw6PDBwcH/9aP/GRtfuf3Hx8cnq7PVVBuQpkLy+M23sNG/9WaLyExERgSAREYmnlNKIiMzIykUkiA21K2UYqIAplZXq1VtFSEQBJCRAAKJjczEL8nIiJaZEYENESEpIgApXMu1SCAzIjMZQYFQzdTW3F2NKgCyxdRqm2ptDcg1rokIGZkAMhJfQKFS1iA8B6oqVQFkywQInt74Pp67cvOPqGpioiaipkYhqaIqApAtkYFEqmopw2wYFovFfD4bhqGUIhRRofwcRbCREZEBpAhFz7mobJA0VS/mpXO3zh0Z01T39++//8GHP/3s88Mnh8fHx9M4ZQuqZEZGg0DNzbRYMVdVE10TVTXzrpSu69yLm5eulNJ1fb9YLLquM9WInKZxqi1aIAFSRFTVzLy4qIHiat4VitTaALp515VSur7vvZT5fLazs7O1tbWYz7uucxczkgABgoAI1kiAWLv+rbvY+PyvrwD4V298KVr7p9/8ETYuf+/LQQEIaEIS0RpaZGZkoGVGDZwTExbXYRiWy+XOxYtXLl/eu7p2+dLO7oUL27P5rC9eSum64mbiqiqkgqyRGWg4J6oiRnMVUgQgRKZxfHb87Pann/7onR98dPPm/v37T58+reOYCIqYipCLrcW1vWsvvvji66+9urd3bTGbd8PgbiqSsZZItGitTbXVbEhATEkCkokaMdV6ulodHT07eHT44ODg3v0HD/YfPjw4ePz06Pj4ZHW2mmoD0lRIZubJN97Gc/a930ZkRABIZGTiOaUkMjIzkkIhCWJD3UopJgpganW1WtVWEQJBABkJIJDYyEz8koyMaJkZEdgQEZIiApDCtVyLBDIjMpMRFAjVTG3N3dWoAiBbTK22qdbWgFzjmoiQkQkgI/EFFCplDcJzoKpSFUC2TIAgVU107fEb/wnPffmzPxdRU6OQVFEVAciWyEAiVbWUYTYMi8ViPp8Nw1BKEYqoUH6OItjIiMgAUoSi51xUNkiaqhfz0rlb546Maar7+/ff/+DDn372+eGTw+Pj42mcsgVVMiOjQaDmZlqsmKuqia6Jqpp5V0rXde7FzUtXSum6vl8sFl3XmWpETtM41RYtkAApIqpqZl5c1EBxNe8KRWptAN2860opXd/3Xsp8PtvZ2dna2lrM513XuYsZSYAAQUAEaySuf/suNt7eu3H9L+9kJgD+y9euREabYorWpvrZmx9iY+fvv5qJCDQgIxPnosU0tWgpShUpqrO+Xy4Xu5cuXbt69erelWtX93Z3dra2t4audxfzMus7L66mFJKSYERGREOCKqKqJuZUVSFFAJ3q+Oz4+PM7d374wx/dvHXz3t27h4eH03gW0USEKiZYLreuXdt76aWXXn/t1Wt71+azRdcXNwMFERkZGRHZYoraMhMkVSkCMANTy9U0rk5PD4+OHj06PDh49O/f/V+w8eLtP352fLI6W021IdJcSB6/+RY2Zm//XmREZLSWyFjLxHNCYqO1AEChkAQBUKhqpSumCmCcptVqVaeaYAKJDPyzzMSvknEuMyMCGyJCUkQAUoiNjAQyI4BECwqEaqZubu7mJiIAIqJOtbVaW8uIzCQJkEIAGYkvoFBIgiIC4TlQVakKIFsmQPD0xvfx3NWP/9hERFXEVVRUSUmAQjMTEVAjgQhRMSt9P8zns34YZsPQdZ2omipFQMoaGZnIjIjMAEGhmbmoqonKmop2vRdzL8VN3Q2RZ9O4v7///vsffPbZnUePHh0fH9dxzICKAgg0CtyKuhU3MxfRNRGIqnvpSum6oeuKe1lz977vh9msK52qAnk2jq22iEQCpJAUMVVxUzNTUytd3wtZWwPEVL2UriuldH3fL+aLnd2d5XIx9H3pSldMRFQpAgrWRHCOWCNx/Vt3sXHnry4D+Fe/9aXI+OA3f4SNq29/NTNbIMFMRKABGZk4Fy2mqUVLUapIUZ31/XK52L106drVq1f3rly7ure7s7O1vTV0vbuYl1nfeXE1pZCUBCMyIhoSVBFVNTGnqgopAuhUx2fHx5/fufPDH/7o5q2b9+7ePTw8nMaziCYiVDHBcrl17dreSy+99Pprr17buzafLbq+uBkoiMjIyIjIFlPUlpkgqUoRgBmYWq6mcXV6enh09OjR4cHBo/0HBw8fHjw8eHT49OjZ8cnqbDXVhkhzIQng+M238Fz5+9+N1hIZa5l4TkhstBYAKBSSIAAKVa10xVQBjNO0Wq3qVBNMIJGBf5aZ+FUyzmVmRGBDREiKCEAKsZGRQGYEkGhBgVDN1M3N3dxEBEBE1Km2VmtrGZGZJAFSCCAj8QUUCklQRCA8B6oqVQFkywQIUtVNRf3x1/5fPPfiT7+joqJKSgIUmpmIgBoJRIiKWen7YT6f9cMwG4au60TVVCkCUtbIyERmRGQGCArNzEVVTVTWVLTrvZh7KW7qbog8m8b9/f333//gs8/uPHr06Pj4uI5jBlQUQKBR4FbUrbiZuYiuiUBU3UtXStcNXVfcy5q7930/zGZd6VQVyLNxbLVFJBIghaSIqYqbmpmaWun6XsjaGiCm6qV0XSml6/t+MV/s7O4sl4uh70tXumIiokoRULAmgnPEC9++i423925c++6nkQmAf/TG9cyMzNoiWtx67UfYuPi9VyKiBhIbiQRaZEwBQEyLeec26/vFcrl7cefa3uW9vb3re3u7u7uLxaLvCiimLGumYkJRAQPSskUiAYies2Je1FREQWRiHMfj09O7n332j+/++NbNm3fufH54+OhsddaykqJCAZdb8+t7115+5eXfev21a9deGGZD3xVVFTJqAoFEZLZoEQ0JkGIKSiQiYqptdTYen5w+PXp2cHh4cHD4H97/t9j4jc/+9Oj05Gx1NtYpWxCA4OQbb2Nj9s4NREZGbS1jIzMjKQQgJIDIzEgAFApJkEKC5laKqyqAcaqr09NxqpnZAin4oozEr5YRkZn4Aq4BFAVAIYCMBDIjADAbANM18w01UyGAFtlqrVOdWs2IzARAEiDOJc4RAIUAhBQRghCeA0VFxEBkywQInt74Pp574fafiKqoKl1EKUIwkBQWKzSlaCYyAxBT7Uo3zOfz2TCbzbquM3dVBSkkSKxlRmZrDZlgqoqZqaqrmylId+v7vhR3czV11cyYxvHe/fvvv//BnU8/ffjw4bOjZ9M0IdPMRJCAqLibu5u7m4uqCkGqqpl3pXT90J3r3U1Fu66bz+deilCAHKeptUACiXOkkBQRU/O10nlXhl5Fa2tIiKi7eem7rgzDsLVcbl+4uJgP7l6Kl65zN1NxJwUiiMA5SQBCvvDtu9i481eXM/JP3nw5M999+W1sfOXHvzXW1mqrtUVEDSQ2Egm0yJgCgJgW885t1veL5XL34s61vct7e3vX9/Z2d3cXi0XfFVBMWdZMxYSiAgakZYtEAhA9Z8W8qKmIgsjEOI7Hp6d3P/vsH9/98a2bN+/c+fzw8NHZ6qxlJUWFAi635tf3rr38ysu/9fpr1669MMyGviuqKmTUBAKJyGzRIhoSIMUUlEhExFTb6mw8Pjl9evTs4PDw4ODw0cGjhwePHx8ePn367Oj05Gx1NtYpWxCAgOTxm2/huf6tN2trGRuZGUkhACEBRGZGAqBQSIIUEjS3UlxVAYxTXZ2ejlPNzBZIwRdlJH61jIjMxBdwDaAoAAoBZCSQGQGA2QCYrplvqJkKAbTIVmud6tRqRmQmAJIAcS5xjgAoBCCkiBCE8BwoKiIGIlsmQFBM3VzdDl79Wzz3yr2/EFGKEAwkhcUKTSmaicwAxFS70g3z+Xw2zGazruvMXVVBCgkSa5mR2VpDJpiqYmaq6upmCtLd+r4vxd1cTV01M6ZxvHf//vvvf3Dn008fPnz47OjZNE3INDMRJCAq7ubu5u7moqpCkKpq5l0pXT9053p3U9Gu6+bzuZciFCDHaWotkEDiHCkkRcTUfK103pWhV9HaGhIi6m5e+q4rwzBsLZfbFy4u5oO7l+Kl69zNVNxJgQgicE7yy392Dxtv79249t1PIzMj+UdffwFAApFoNT78jbexceF7r0y1ZUYEAAkgExkRDUKUvnTezboyDMNiPr9wcXvv8uUrV65e27u6u7OztVyUUkCoSDE1U6qKqpCRiMwEGkgRteJezIuZUhVAbTGN48nJ6b27d9//4J8+unnrzqefHhw8PBvPaqskCAiwWCyu7V195ZWXv/61r12/fn0YZl1XVFWAiEACBHItWmQmSJprQmq0qbZa2+lqPD5dPT169vjwyaPDJ//h/X+Hjd+8+62T49XpeFanqbaaEUCe3ngHG/MffDMzo7XaWsa51gIbFAoJIDIzAiCFSqGQIFVcrXSFFCDG2lYnJ+M01ZYZmYJfJyPwBYlzmYkNkgAIUAQg/llmBABmA2C6ZsWLuZm7UgC0jDpNdaot2lpm4gtI4udIoZAEKSQI4RoAFRU1ENkyAYKnN76P5750509FVUWFSlGhQpCAUL2YqpKaQG0NEBHtuzKbL+bz2WI+H/rBuyIiyAxARLCWGRGttcgEU4VmpmruZmpCevGh671zVXVVM83EOI7379977733bt++/eDBw6Ojp9NZJeBuqgpJVXU3dzd3N6dQqKJU1WLupfTD0HVD3xU1I6WUspjPvRQRQeZUa0QiCUBI/IyQqqbe9V3X9aXrVLS2ACAUNXUvQz8Ms2Frudze3u66DoSbdX3fFe86987cqUIAEUgkkBR++c/uYePOX10G8K9/92UAP37x+9h45YM3z8ZxPJumWqfaMiMCgASQiYyIBiFKXzrvZl0ZhmExn1+4uL13+fKVK1ev7V3d3dnZWi5KKSBUpJiaKVVFVchIRGYCDaSIWnEv5sVMqQqgtpjG8eTk9N7du+9/8E8f3bx159NPDw4eno1ntVUSBARYLBbX9q6+8srLX//a165fvz4Ms64rqipARCABArkWLTITJM01ITXaVFut7XQ1Hp+unh49e3z45NHhk0ePHj96/OTR4ZOnT5+eHK9Ox7M6TbXVjACSIiffeBvP9W+9WVvLONdaYINCIQFEZkYApFApFBKkiquVrpACxFjb6uRknKbaMiNT8OtkBL4gcS4zsUESAAGKAMQ/y4wAwGwATNeseDE3c1cKgJZRp6lOtUVby0x8AUn8HCkUkiCFBCFcA6CiogYiWyZAUEyLFXN/8Op/xHNfvf8XFBUqBAkI1YupKqkJ1NYAEdG+K7P5Yj6fLebzoR+8KyKCzABEBGuZEdFai0wwVWhmquZupiakFx+63jtXVVc100yM43j//r333nvv9u3bDx48PDp6Op1VAu6mqpBUVXdzd3N3cwqFKkpVLeZeSj8MXTf0XVEzUkopi/ncSxERZE61RiSSAITEzwipaupd33VdX7pORWsLAEJRU/cy9MMwG7aWy+3t7a7rQLhZ1/dd8a5z78ydKgQQgUR+5X+4h423925c++6nkQmAf/j1F7AmikCN9sHL38fG1t+9UttUGyIygQy0RAaywVxnQ7eYD/OuG2bDbBi2trYv7Vy4dOny3pXLuzs7y+Wi6zsSKuJubmqqIgpRABFIIABRVTf14l7MnWKJnGo7OxtXp6f379378OaHP7l16+OffHJw8PBsPKtRgURmRizn870rl1/+6su/88YbX7p+fRj6UoqbgYIIrFGAQGYGAhChmgcwjdPU6lTbapyOT8+Ojo4fHz55fHj4/7z/v2Hj1Xt/dnJyslqdTW2aam21RuT4Bz/ExvwH38zMaK22FrW1jIwACIBCISMzI4EEQBGlrEEoFHPrilM0I6c6nZ6cnk1jbZERiV8r8QsyE8+RBECcowh+UUZgrTUKhOruxd2Lu5uIAqittVqnWlutrbXMBJCR2KAQAEmAFAIQUkQAEIQQgFDNHUS2TIDg6Y3v47kv3flTUVUqRYVKWaNQRdVcKQqwZUZrACg29P18sdhanhtms650IpxqjUxVFRBAy4jWYg0hhKi6mZubnjPzvu/Mdc1V1RSJaRrv3bv33nvvffKTj/f3Hxw9ezqdjYR4MTElYaZqbqbFirmqGoWmqrbmXSldP3TnelOlaFd8Npt5KaYama01JADhBoDMBEkVN/PSd11Xuk5Fa2tIUNRUzUvfd/P5fDFfLLeWZl7rJJSu877v5vNhGPph5mYCICIjz5F48Tv72Pj8r68A+Fe/8xKAd1/8Pja++tHvrc7G1Wqcxqm2qTZEZAIZaIkMZIO5zoZuMR/mXTfMhtkwbG1tX9q5cOnS5b0rl3d3dpbLRdd3JFTE3dzUVEUUogAikEAAoqpu6sW9mDvFEjnVdnY2rk5P79+79+HND39y69bHP/nk4ODh2XhWowKJzIxYzud7Vy6//NWXf+eNN750/fow9KUUNwMFEVijAIHMDAQgQjUPYBqnqdWpttU4HZ+eHR0dPz588vjw8ODx4aPHTw4Pnzx5+uzk5GS1OpvaNNXaao1IVTm98Q6e6996s7YWtbWMjAAIgEIhIzMjgQRAEaWsQSgUc+uKUzQjpzqdnpyeTWNtkRGJXyvxCzITz5EEQJyjCH5RRmCtNQqE6u7F3Yu7m4gCqK21WqdaW62ttcwEkJHYoBAASYAUAhBSRAAQhBCAUM0dRLZMgKCYFivuvv/qf8Rzr9z/C6FS1ihUUTVXigJsmdEaAIoNfT9fLLaW54bZrCudCKdaI1NVBQTQMqK1WEMIIapu5uam58y87ztzXXNVNUVimsZ79+699957n/zk4/39B0fPnk5nIyFeTExJmKmam2mxYq6qRqGpqq15V0rXD9253lQp2hWfzWZeiqlGZmsNCUC4ASAzQVLFzbz0XdeVrlPR2hoSFDVV89L33Xw+X8wXy62lmdc6CaXrvO+7+XwYhn6YuZkAiMjIfPE797Hx9t6Na9+9AyQA/jevXQFIlUxmy5u/+TY25n/3UptaaxmRkWiBaMgEgdL5cjEs5rPFMMxnQ9f3i/l898L27u7u5cuXdy5e3Npa9F1HwkxLKW5qZkJVlQRrJoAMUkXUtLh5MTOqA5jqdDaOq9PT+/sPbt269ZNbN29+dOvhwYNxHFvURCIzI+az4eqVK7/x8otff+ONL12/Phv6riumRhEEzinOJdYygiJiFolpqlNttbXVOJ2uzp4+Ozl8cvjo8ZN//96/w8a/uPunx8cnp6uz2qY6tdoagPEPfoiNxQ9/P9Zaq61FbbU1IHGOFGIjIzODayJKWaOKqYpq8SLCiJzqdHJycjaOtbZoCUHiHEn8oszEr0ESG8Sv1xoBUXW34sWLu5uqZiKiTVOdam21ttZyLTIR2CAEAIVrACkUkiCFAAgCoKq7g8yWACg8+cb38dwLt/9EVCmqVBGjUFXNTMxVFefYMqO1SJjZ0A3LrbXl1nJrmM/6rgN5No4RYWZCUZXIbFOt0TIrAIqYqpubmaqaWlfci+uaaTEFUKfp3r17//jjH9/++JN7+/ePnh7VcSJRShFRKFRE1UxV3VxNTNxcVN3NvXSldN3gpXSlmKmIdl03G2beFVUFEBEAhCoUkAAiAgRIM3PvvBQvRURbCwAqYmbupe/7+WKxmC/WQKxWqzpVVfZ9v9xabm/Nt7bmXe+qBNBaRiaQL/2bB9j46d9cBfjfvn4N4Ee/8Q42Xnzvd1fTNE7jOE5taq1lREaiBaIhEwRK58vFsJjPFsMwnw1d3y/m890L27u7u5cvX965eHFra9F3HQkzLaW4qZkJVVUSrJkAMkgVUdPi5sXMqA5gqtPZOK5OT+/vP7h169ZPbt28+dGthwcPxnFsUROJzIyYz4arV678xssvfv2NN750/fps6LuumBpFEDinOJdYywiKiFkkpqlOtdXWVuN0ujp7+uzk8Mnho8dPHhw8fvTo8ePHh0+Onh0fn5yuzmqb6tRqawBM9eTGf8Vz/Vtv1taittoakDhHCrGRkZnBNRGlrFHFVEW1eBFhRE51Ojk5ORvHWlu0hCBxjiR+UWbi1yCJDeLXa42AqLpb8eLF3U1VMxHRpqlOtbZaW2u5FpkIbBACgMI1gBQKSZBCAAQBUNXdQWZLABRStFgx1wev/i2ee/nuvxExClXVzMRcVXGOLTNai4SZDd2w3Fpbbi23hvms7zqQZ+MYEWYmFFWJzDbVGi2zAqCIqbq5mamqqXXFvbiumRZTAHWa7t27948//vHtjz+5t3//6OlRHScSpRQRhUJFVM1U1c3VxMTNRdXd3EtXStcNXkpXipmKaNd1s2HmXVFVABEBQKhCAQkgIkCANDP3zkvxUkS0tQCgImbmXvq+ny8Wi/liDcRqtapTVWXf98ut5fbWfGtr3vWuSgCt5Ve+cx8bb+/duPbdz8gEyBtfXkJAChItcPd33sPG7L+8GC1qZlbUlpGoDQKose9ssTabLWfDbBi6vl/O5xe3t3d3di5fvnThwoXlcl5KIeFu874vxc2MoiqSYIuMjEgBKSpia04zqgDMxDTV09PT+/sPPv74Jzdv3vzwgw/u7++3NrVoxFpmi8V8dvXK5VdefunrX3v9hevX5sPQdZ2ZUogABEIBEAlENKQANEPKVKeptWh5Vtvpajw6Pj48fPLg0eP/+91/i42v3v7vjo6PT87O2lgbQkCqnN54BxtbP/r9FhmtTbW2WluLzABAEiB+LtcAiKpSxFQopmqu5oVARI7TeHJ6slqt2tQiEcTPiAh+UUTgC0QEv4Qkfgmx0RoBUXU3dy+luJuIAqittVqncZpazYhci0xEBNZEQAgACtcAUigkQQAUAqCom1MlWwKkyMk3vofnrn78xyKiokJVdRFRt1KKmQkFYEO2QGtNSLcyDMNye2t7a2u5XA7DrOs7ZJ6uVpFZ3NXMVAHUWqdWa50yA4CKupupuZmampm7m2nxUooJMI7jvXv3fvzuu5/85OP9/QdPjp62cSSkdIVKEARUVFRMTUzcXE3dSleKFyulL6WYFzc1K2t9382GWek7UwUZEQKKKCgEExkRQAYpFHc3c7UiIhEpQlE301LKbBjmi8VivpjPZ5l5dPTs9OR4NZ6VUi5evLC7e3H30sWtxdw7NZXMbJFrL35nHxuf//UeyW9+ZRuCz7/+Pjau/uC1qbVaW60tWtTMrKgtI1EbBFBj39libTZbzobZMHR9v5zPL25v7+7sXL586cKFC8vlvJRCwt3mfV+KmxlFVSTBFhkZkQJSVMTWnGZUAZiJaaqnp6f39x98/PFPbt68+eEHH9zf329tatGItcwWi/ns6pXLr7z80te/9voL16/Nh6HrOjOlEAEIhAIgEohoSAFohpSpTlNr0fKsttPVeHR8fHj45MGjx/sPHx08eHjw+PDJ4dOj4+OTs7M21oYQkCoUOfnG23huePsbU62t1tYiMwCQBIifyzUAoqoUMRWKqZqreSEQkeM0npyerFarNrVIBPEzIoJfFBH4AhHBLyGJX0JstEZAVN3N3Usp7iaiAGprrdZpnKZWMyLXIhMRgTUREAKAwjWAFApJEACFACjq5lTJlgApIqqmKmaPXvtbPPeVz/5c1UVE3UopZiYUgA3ZAq01Id3KMAzL7a3tra3lcjkMs67vkHm6WkVmcVczUwVQa51arXXKDAAq6m6m5mZqambubqbFSykmwDiO9+7d+/G7737yk4/39x88OXraxpGQ0hUqQRBQUVExNTFxczV1K10pXqyUvpRiXtzUrKz1fTcbZqXvTBVkRAgooqAQTGREABmkUNzdzNWKiESkCEXdTEsps2GYLxaL+WI+n2Xm0dGz05Pj1XhWSrl48cLu7sXdSxe3FnPv1FQy84U/u4eNt/duXPvuHYDnfnuvx0YAmXj0zVvYmP2Xl7JlzYjIOmU01ISp9L3Oh2GYzebDMO9/plvOF7sXtnd3dy5fvrx9YXsxm5kbkG42H/pS3EzXSAXYIlsiEiBFRUxVi6hCCQqpU51Ojk8ePnzwySeffPjRR+/++N37+/dqq4igkji3nM/29q5+9aWXfpcryqcAACAASURBVOu1V790bW8Y+tIVV8dzIgQQkYlokQBUNMDWskbLwNjaapyOjk8eP35y8OjR//mP/ys2Xrn9R0fHz05OxqnVzDQzUT1+8y1sbP3o91tktDbV2mptreVaJIUASAIEMlpQKKpKEVNTFVVXMzcRqbWdjWfHJ8er1dhqa5EQgARAnBMRbEQENhI/R0BE8EsiAs+JCEkARKIFAaq4WSnF3c1dKQBqtFbrdK5mRkYmIgL/PxGsEUIhAJIAKRQSGyLqpYhoRgKkyMk3vofndj/6QyWpauKqbqZqpfTFzUAB0BJxLkW0K2U2m21tbS03hmFw98g8Xa0AFC/upmYAWq3TVKc2RqsASJqZq4mq25p68a50pZS+GIBxHH969+677757++NPHjx8cHT0bJomAsWLqUKwRgEpomqmbq6mZc2Kd6V4UTM3F1V3K6WfDf1sNuu6Ts2EjEz5/+iCt1+9zvw+7N/f4XnW4V3rPewDN08iRWkkzcGTmVHkZGK4NVLYGMNo3LhpgBZILnpfIGkKtL3oZQHDSNHetAj8H/SiQVDf1gjgwo0kDpWRnKE64kgiKZKiRHJzn97jWs/z+3XtV0NrZop8PiBmATEG7maW3RwAsYqyqKgyCwAmCTGEEEPQqqrGzXjUNqO6MrOTk5Pjk5OT42MR2t3d2dvfOzi4sLMzqUdVDMpMgGfza3/0JbYe/dlFIvrepRrA07c+wdbs3VcNnrOZwbMnNzNPvVtGcqhwWcqoqqq6HlXVqPxK0Y6a3elkd3dnf39/Mp00da1BAQ+qo6qMMajKgEgAyubZYQ4QsTCriEQWgRCIiaRP/XKxfPbs6b179+78/Oe3f3r7yydfpJxgRkKEc+2ovnjx4NWXX/7ON9+4euliVZWxiEECXmAmAGbusGwOQFgMlLMny27ocl53/dlieXR0cvj8+ZdPD588e3Z4+Pz4+PRsMV8uuz4nd1dVFiGi+fdv4oXq1pt9SjmlnLMPzIkJABEBBLhlIyYWEWJWUREWCaIalJlTyptus1gu1usup5zNwQARAMI5ZsaWmWHL8QsEMDP+f8wMLzAzEQEgOLIRQMJBNcYYQtAQhBhAspxT6s8ld3Nzh5nhrzFjQGBiAkBEABETE2GLWUKMzOLmABEzMakIixx9+y/wwpX7f08kqIpojGUMqiAGkB12zpmliLGu6/F43G5VVRVCMPfVeg0ghhiCiiqAnFLfpz53lhMAIlLVIMoiQQcSYihiEWMsowLouu7zx49v3759/+69p8+enp3N+74nIIaoImAMiEHELKIqQYOoxIHGUMQYoqgGDSwSgsZY1lVZ13VRFKLKRObOIGYBMQbuZpbdHACxirKoqDILACYJMYQQQ9CqqsbNeNQ2o7oys5OTk+OTk5PjYxHa3d3Z2987OLiwszOpR1UMykxX/pPH2Lp18a2DP33AtPUbFwt3uJk7HDj54T1sNf/mVTNPOVu2Prtnt4xYhMmkaUZ1HIRQxViVZVlVk3GzN9vZ29vd39ubTCZ1VYiKZ1PhIkYNElQGLGIQNzOHAeCBsKpoYBGwEBMT9ynP5/PDw2f37392586dDz74yeePP+/7HkAIwsxCNJ60Vy9deuXG9W+/9vqlSwdVWcQYAgvABgPAYMDMPdsgu4FEAE7ZHHAgZV/1/XK5en58enh09M9/8l9j67WHPzqbL1brdd8nB1iDspx87x1sTT74YbJsOfcp5ZRyzpbNYQAITEwDH5gTExGrCKuoCIsEUQ3KzCn1m3U3XyzW3Tr17m4ggBlEAAi/zrHljgER4ReYGYCZAXB8jQBmJiK4wzIBxBQkxBg1BA2qwuYws77vc59STpbNYQDMMHA4AAJhixkEBkBMA4AAEJOQSIgq4nACEcvyzXfwwvTD3yFmIgkhBokSJAxiFFUiBsGd3N0cIlIU1WhUt+24bUb1aFQUhYqYeZ96EBUxhhCYBYBZ7vu+S13OycyYSFhUhEWCSgihKGJRlmVRVkUEsFqvHj9+fPunP713//7zw+fzxcJST+AYIwsRMwgMIgYRq4poCCoxRg0xBA0aVJREmFlFY4hVXdWjUVEUqsrM7g6ASQAC4O6Ws7kZBswszMKqQswi4Vwsiqgayqoat82gHjVwOzk5OTw8/PzxY8v9dDq9cGH/4qWLFy7s7exMR6OKmUTI4Vf+8AtsPfqzi0T03UuVO57/5qfYGr/zMs6xGZl5ytmy9dk9u2XEIkwmTTOq4yCEKsaqLMuqmoybvdnO3t7u/t7eZDKpq0JUPJsKFzFqkKAyYBGDuJk5DAAPhFVFA4uAhZiYuE95Pp8fHj67f/+zO3fufPDBTz5//Hnf9wBCEGYWovGkvXrp0is3rn/7tdcvXTqoyiLGEFgANhgABgNm7tkG2Q0kAnDK5oADKfuq75fL1fPj08OjoydPD58+ffbs8PDo+PRsvlit132fHGANykJMx999Gy9Ut97sU8op5Zwtm8MAEJiYBj4wJyYiVhFWUREWCaIalJlT6jfrbr5YrLt16t3dQAAziAAQfp1jyx0DIsIvMDMAMwPg+BoBzExEcIdlAogpSIgxaggaVIXNYWZ93+c+pZwsm8MAmGHgcAAEwhYzCAyAmAYAASAmIZEQVcThBCIWYhISEpz8xl/ihYNP/yBIlCBhEKOoEjEI7uTu5hCRoqhGo7ptx20zqkejoihUxMz71IOoiDGEwCwAzHLf913qck5mxkTCoiIsElRCCEURi7Isi7IqIoDVevX48ePbP/3pvfv3nx8+ny8WlnoCxxhZiJhBYBAxiFhVRENQiTFqiCFo0KCiJMLMKhpDrOqqHo2KolBVZnZ3AEwCEAB3t5zNzTBgZmEWVhViFgnnYlFE1VBW1bhtBvWogdvJycnh4eHnjx9b7qfT6YUL+xcvXbxwYW9nZzoaVcz00h99ga1bF986+NMHTFvfv9q4Wd+bezbg9If3sDV59/WUz6U+Z/OcMajLand3WtWViATRGKSOZVXX00m7u7Ozt7uzv7c3Ho+rshBhNxOmoBJUNciASYzJDI4tFtKgqqKBRUiEmQDuU14sF4eHh48ePbrz0c9uvXfr0aOHKfUAVFVUoupsOrl6+fIrN65/65VXDw72qzLGEJgYBHPAjYnNLZtbzuZmABMDkt3c2YHerOvTYrU6Pjs7en78x+/9U2x964s/mM8Xy9Vm0/dm0CAEPvneO9hq3v9blrK5pT5lywPL5jAzDJjBLADcHACLqAirqIioBlURYeaU+tV6PV8sNutNSubYIhAziAi/zgG4468RASB8zQG4uxkAYgYRAcxMMJgTQEwqoiGGEKIGEvaBWd/1fU6Ws1nGlhkGDsevIhAAZhCYmADQQCSEyCwAmJiZlm/exAujf/fbRFAOIRQaQtAQY2SVAYgABmA4p6pFrJpm1LRt2zR1XYcYRQRAzhlERYyiqiIAUkp9Sn3qLCczA8BETEzMKlKUsSzLUV0XZVUVEW6r1erhw0e3P7z92f3Pjk9OVstFzi5EGoKKsDIRAWAGk7CyioiGEDSoBg0syiLCDCJmCRqKsmibpizLEAKIsMUQEMHdzHLO5m5wgJmEWUiYRQcxxqosNUQRqcqqaZpJ2zbjMYDT09Nnz5589uCz9WrVtKPZbHZwcHDx4MLBwf5k0lZ1oSpEuPyHj7H16M8uEtEPXhq72ZM3f46t8TsvMwkxm1HK51Kfs3nOGNRltbs7repKRIJoDFLHsqrr6aTd3dnZ293Z39sbj8dVWYiwmwlTUAmqGmTAJMZkBscWC2lQVdHAIiTCTAD3KS+Wi8PDw0ePHt356Ge33rv16NHDlHoAqioqUXU2nVy9fPmVG9e/9cqrBwf7VRljCEwMgjngxsTmls0tZ3MzgIkByW7u7EBv1vVpsVodn50dPT9++uzw6bPnzw6Pnh8fz+eL5Wqz6XszaBACE9Pxd9/GC+WPf5D6lC0PLJvDzDBgBrMAcHMALKIirKIiohpURYSZU+pX6/V8sdisNymZY4tAzCAi/DoH4I6/RgSA8DUH4O5mAIgZRAQwM8FgTgAxqYiGGEKIGkjYB2Z91/c5Wc5mGVtmGDgcv4pAAJhBYGICQAORECKzAGBiZgIJMwOYf+//wQv7P/99DSFoiDGyygBEAAMwnFPVIlZNM2ratm2auq5DjCICIOcMoiJGUVURACmlPqU+dZaTmQFgIiYmZhUpyliW5aiui7Kqigi31Wr18OGj2x/e/uz+Z8cnJ6vlImcXIg1BRViZiAAwg0lYWUVEQwgaVIMGFmURYQYRswQNRVm0TVOWZQgBRNhiCIjgbmY5Z3M3OMBMwiwkzKKDGGNVlhqiiFRl1TTNpG2b8RjA6enps2dPPnvw2Xq1atrRbDY7ODi4eHDh4GB/Mmmrurj+D55g69bFtw7+9AHT1t/+xr6l3Ke+T9ndjn7zE2zt/Ntv5z53/aZP2ZIDxMx1Xe/OpmVdMTEzBZaqKOu6bNt2b2e2u7Ozu7MzHrdlWagIA8QURFRZVZhlYMTmOEdEIqJBQgwhiiqLgDi7pT6t1uuTk+Mvvvzizkcf3Xz35mcPP+v7HnAVCSEUUWfT6fVrL71y7eVv3Lh2cW+3KEJQZSLAs5m5C5E7kpmbZTMHSAQgdzKHOVK2rs/L9fp4Pj86PvnjW/8EW99//vfPzhaL1WrTp5wzQCCcfu8mtqpbb+Zs5mYpZzfL2c2zGQCHE0hVALg5ABZREVYJqiwSVEUEQM55uV6dnZ1tus4zHO4gIhAziAAQvubYcnczAMSMARF+mbtlwwssDCICGIBnApiERUIIMQQNysQAUh6kvk+WczbDL3E4AHfDryJiAjGDwMREIkECiRCImIV49dZNvFC9/3cAiIYYihjLGM45DUAkYADkBmIWlbKo6lEzGbdN04zqUVEWLEJE5s7MKioqTGTuOeWUU859ygl+Dg7COVUtyljXdTMaVVVVxGiWl4vlw0cPb9/+8OGDh2dnp+vVGgCDQgyiElRImWnA52TAAxURlgGLMAuTACAmEBdFHLdtVVUhBBFxc4CIGYZBdrOczSwTGEwkzAImVQ2xKGJRVmXQQOCiLNp2PBm30+mMRRbL+bNnTx98dv/07EyER3U925nt7+9fvXJx78LubDopy5IFV/7wMbYe/dlFAH/n9YuW8sPf+HfYmt16jQfE2Sn3ues3fcqWHCBmrut6dzYt64qJmSmwVEVZ12Xbtns7s92dnd2dnfG4LctCRRggpiCiyqrCLAMjNsc5IhIRDRJiCFFUWQTE2S31abVen5wcf/HlF3c++ujmuzc/e/hZ3/eAq0gIoYg6m06vX3vplWsvf+PGtYt7u0URgioTAZ7NzF2I3JHM3CybOUAiALmTOcyRsnV9Xq7Xx/P50fHJ4eHRs+fHh4fPT05Pz84Wi9Vq06ecM0AgmGP+/Zt4Qd/5G5ZydrOc3TybAXA4gVQFgJsDYBEVYZWgyiJBVUQA5JyX69XZ2dmm6zzD4Q4iAjGDCADha44tdzcDQMwYEOGXuVs2vMDCICKAAXgmgElYJIQQQ9CgTAwg5UHq+2Q5ZzP8EocDcDf8KiImEDMITEwkEiSQCIGIWYhJhAgOLL//Nl7Y+ej3YixjOOc0AJGAAZAbiFlUyqKqR81k3DZNM6pHRVmwCBGZOzOrqKgwkbnnlFNOOfcpJ/g5OAjnVLUoY13XzWhUVVURo1leLpYPHz28ffvDhw8enp2drldrAAwKMYhKUCFlpgGfkwEPVERYBizCLEwCgJhAXBRx3LZVVYUQRMTNASJmGAbZzXI2s0xgMJEwC5hUNcSiiEVZlUEDgYuyaNvxZNxOpzMWWSznz549ffDZ/dOzMxEe1fVsZ7a/v3/1ysW9C7uz6eS1/+IIW7cuvnXwpw+YCAD9B9+8lHNed5ucUs5++NbPsXXh/e/0fd5s1l3KbiDmoFrXo53ZrK5KAEKkrDHGuq7GTbMzncymk+l02rRNXZVFCCyszCqswiLMoiAC2NwMDAKLsMYQY4hRVUkFxG5IKXd9dzafPzt8cufOnZvvvnvv3v2u79yNSULQoox7s50b1196+dq1G1ev7O9MiyIEYRDDLeXs7kzkgJuZW84OQFRAbM7myNn7nDd9Wq7Xp/PF0cnpH7/3T7H15tF/ejo/WyxX6y71KbnDzZdvvYct/Td/w2Hn3N3MB+YOMwPgAKkKALNMYBZREVYJqiwSVEUEQOrTcr06O5tvNhsH4HAiAEQgZhARvuYA3AG4GQBixleI8BV3NzN3vMBExExEDFjODLCwiIZzKqLMBCCb5T6lnHJOZvhlDnc3GAYOxxaBwCBiAjGDwCwiQYUFxAMhWb11Ey+EW3+LGCqhKKpYlGVRCHMyA0DMADsIW6palGUzasaTybhtm6apylJUmQUACwkLmACCWZ8HyXIyy+YO92yG7AYLqlVZVKPRuGnKqi6KaKmfz+cPHj788PbtBw8fLeeLrusYRMJBREOIhYoqQCwszEzMKkxEzAyQCINYBGBmDMxQlEXbjkd1FWJUEZwjJgbI3G2Qc3YHASCQMDExq2pRlEVRlFWlojCEGJummU4ms92dIsbVenV09Pzho0cnx8+7rmPhUV3v7u5cfenKpUsXDw7227YR4Zf+6AtsPfg/LzLhd759Ned8942fYGv31usaFCB39H3ebNZdym4g5qBa16Od2ayuSgBCpKwxxrquxk2zM53MppPpdNq0TV2VRQgsrMwqrMIizKIgAtjcDAwCi7DGEGOIUVVJBcRuSCl3fXc2nz87fHLnzp2b77577979ru/cjUlC0KKMe7OdG9dfevnatRtXr+zvTIsiBGEQwy3l7O5M5ICbmVvODkBUQGzO5sjZ+5w3fVqu16fzxdHJ6eHz46Pnx4fHR8fHZ6fzs8Vyte5Sn5I73BzA8q338AL95bfN3c18YO4wMwAOkKoAMMsEZhEVYZWgyiJBVUQApD4t16uzs/lms3EADicCQARiBhHhaw7AHYCbASBmfIUIX3F3M3PHC0xEzETEgOXMAAuLaDinIspMALJZ7lPKKedkhl/mcHeDYeBwbBEIDCImEDMIzCISVFhAPBASYgYR4MsfvIMXZj/73ViUZVEIczIDQMwAOwhbqlqUZTNqxpPJuG2bpqnKUlSZBQALCQuYAIJZnwfJcjLL5g73bIbsBguqVVlUo9G4acqqLopoqZ/P5w8ePvzw9u0HDx8t54uu6xhEwkFEQ4iFiipALCzMTMwqTETMDJAIg1gEYGYMzFCURduOR3UVYlQRnCMmBsjcbZBzdgcBIJAwMTGralGURVGUVaWiMIQYm6aZTiaz3Z0ixtV6dXT0/OGjRyfHz7uuY+FRXe/u7lx96cqlSxcPDva/+18usXXr4lsX/sUDYmIC/c63L1lO6/Wm7zszPH3rE2wdvP/tvk/r9abP2UEqUsSyGY2m00lZlgIWIZFQxFgWRTMatW09bsfjtm0Go7quiiLEoKJMwsxCxOIEOJKZZc8gVpYQVTXEGEJgjcTkYDPLlter1dHJySeffPz22+/cu3d/s16lnGWgUsS4uzu7cf3a9atXrl++NJuOq6AsDMDNLGe4ExEIvmXmAIkKsQCUHDnbpkurrluu1ifz+fHJ/E/e/2+w9f2jv396erpYrFabru9yhruj/+EH2PK/+JbDAR8AcHNsOQxbBAbgMAJYVEVZJaiySFAVEQdS3y1X67P5fLPZuAMgEAAiAjGDiABmBmBmDsAdgJvhBWLGgAjuACybwwcYEDGIhYUIDrPMADGLStAQNEgQAgEwt5y+kh0OgEDYcri7weBwd8MLREwgEiYQM1iERZWFWIhYRVZv/Rgv6I/fAlFQLWJVVGVdjojQ9X0yFyaAzeCAw4KEsqyathlPp9PJZNKOq7ouYpQgIGICiACYuWXLOWXL5hnm5m5uni3lbJaVpazKdjRq2rauqxBiTv3pyemDBw9uf3j7888/XyxWOfcMGahwCDGUQURYBsQsPKAB468RMZiEGIytoizadjwa1UVRsAgTAcQk2DKzlDO+QuQOEBOxSiiqsizKqipZxPrMok3TTKeT/Qv7ZVn1fXd6evr06ZPD54fzs5NN17HQZDK+euXq1auXrly9MptNy7K4/g+eYOvBv7rITH/3u1csp49fex9bF269JiEwOLn1fVqvN33ODlKRIpbNaDSdTsqyFLAIiYQixrIomtGobetxOx63bTMY1XVVFCEGFWUSZhYiFifAkcwsewaxsoSoqiHGEAJrJCYHm1m2vF6tjk5OPvnk47fffufevfub9SrlLAOVIsbd3dmN69euX71y/fKl2XRcBWVhAG5mOcOdiEDwLTMHSFSIBaDkyNk2XVp13XK1PpnPj0/mR8enz4+Onh0dH5+cDhaL1WrT9V3OcHe4efqtv8IL9hdvuDsAN8eWw7BFYAAOI4BFVZRVgiqLBFURcSD13XK1PpvPN5uNOwACASAiEDOICGBmAGbmANwBuBleIGYMiOAOwLI5fIABEYNYWIjgMMsMELOoBA1BgwQhEABzy+kr2eEACIQth7sbDA53N7xAxAQiYQIxg0VYVFmIhYhVhJgAdtjqzZt4Yfrhf1RUZV2OiND1fTIXJoDN4IDDgoSyrJq2GU+n08lk0o6rui5ilCAgYgKIAJi5Zcs5ZcvmGebmbm6eLeVslpWlrMp2NGratq6rEGJO/enJ6YMHD25/ePvzzz9fLFY59wwZqHAIMZRBRFgGxCw8oAHjrxExmIQYjK2iLNp2PBrVRVGwCBMBxCTYMrOUM75C5A4QE7FKKKqyLMqqKlnE+syiTdNMp5P9C/tlWfV9d3p6+vTpk8Pnh/Ozk03XsdBkMr565erVq5euXL3yw/+KsHXr4lsX/sUDAjET/d53L/d9Wq/XKSVze/yDT7B16f1vpZRW6y4PQCpSllXTNONxOyprJhmoSgihiGFUVaPRaDxuppPppB1PJm1dVTGoMgvATCzMhOQwSynlbJ4BZiYVlRhiDEUMRVSJDmTAzVbrzenZ6aeffvruu+/evXt/uVqmvgcgGsqy2NvbfeXla9euXrlycGF30hZBRcjNPOeUEtyIWZiJCAS4A0TMTuTmyTxl7/q06rrlan06Xx6fnv3JB/8ttr7z5A9OTk/ni9Vy3eU+u8MN/jsfYiv/6zccDjjOmeMXzABkgJmJcI6YWVVZiElFJWhU1RBzzpvNZrVazc/mXerdARAIABGBADCDiPA1dwAOMwAOwEGEc8wwc8AdDh8AoAGICQRmwNwAMEFEQihCUAnKRA73bH1OlnNK2TFwAAQC4HDP5nB3c8NXiDEgYgKBMWARFdWgIkosQrx+6xZe0B//TRYJIRaxjEWMWoLRdb07iODOni27OyGGOKrqdjyezKaz6XQ2mdajUVEWosoMEBgwAO7ZzHK2LcAB5Gx96vvNZrXZCNGorpumadumqqugMec8X5w9fPDg/Q/+6tHDR6vVMuUcSFiFSUIRqqoMMQoLCzGLEINwzpHd3BwAMYlIkMBMAIeodT2q66qsqqABBCYCWERUBIBly7blDiIQM0nQEGIsYgxFZHB2C6r1qJlMJnu7u3VVmftytTo5ev708NnTJ1/M52dmVo2qCxcuXLt69caNGxcu7jdN8+p/foitB//qIoN+9ObVvk8ffeMn2Lry/usi6qCULaW0Wnd5AFKRsqyaphmP21FZM8lAVUIIRQyjqhqNRuNxM51MJ+14MmnrqopBlVkAZmJhJiSHWUopZ/MMMDOpqMQQYyhiKKJKdCADbrZab07PTj/99NN333337t37y9Uy9T0A0VCWxd7e7isvX7t29cqVgwu7k7YIKkJu5jmnlOBGzMJMRCDAHSBidiI3T+Ype9enVdctV+vT+fL49Ozw6PTw+dHTw6Ojk+OT09P5YrVcd7nP7nCDAfQ7H+KF9K9fwzlz/IIZgAwwMxHOETOrKgsxqagEjaoaYs55s9msVqv52bxLvTsAAgEgIhAAZhARvuYOwGEGwAE4iHCOGWYOuMPhAwA0ADGBwAyYGwAmiEgIRQgqQZnI4Z6tz8lyTik7Bg6AQAAc7tkc7m5u+AoxBkRMIDAGLKKiGlREiUWIickN2b3/zVt4Yeej341FjFqC0XW9O4jgzp4tuzshhjiq6nY8nsyms+l0NpnWo1FRFqLKDBAYMADu2cxyti3AAeRsfer7zWa12QjRqK6bpmnbpqqroDHnPF+cPXzw4P0P/urRw0er1TLlHEhYhUlCEaqqDDEKCwsxixCDcM6R3dwcADGJSJDATACHqHU9quuqrKqgAQQmAlhEVASAZcu25Q4iEDNJ0BBiLGIMRWRwdguq9aiZTCZ7u7t1VZn7crU6OXr+9PDZ0ydfzOdnZlaNqgsXLly7evXGjRu/9z+MsXXr4lv7/+tnLMwg+o/ffCn1/XK9zn0i0Ce/8TNsvfTBG31vXe5Tn1N2Eq3Lsm5G42ZS12XQKMLCIiwhal1V7Wg0m0339/Z2d/d2d2d1WWFgGZYJUBUAfeoHm743MxCBCACpaIhVVTVtG2I0Z/Occl6t1ienZ598evfWe+/dvXvv7Gy+WXcZHoKORs2FC/uv3rh+7cqVywd7s3FbFqqAeUp933Ud3INyENUYhBlwGADqLfdd36ec3PtsXcqrdXe6WD4/Ofvnf/XfY+uVe3/36ORkvlhtui4nbHH43Z9jK//5Gw5zDAyAwd0NgJsDhgGLCrMQi6oGIvLsJFyUZQxxkHJanC0Wi+Vqver7nogAuAOErxBAIBAIDCYGDAN3A2BwDBxfccc5B+COLQKICCDCOQIcAxeRoEUMMRZKxObmlvs+W84ON8vmDjMwAwaDw93NDV8hxq9xAwtzkBhiiJFFRXj9g1t4QW+9VRRlEWOIkVkBuMMdADGzZevWvcGZqSjLcTOZTmeT2WRnNptOZ23ThLIIIiQgBgNEBBDg2czc4A4QMaWUVqvV/Gx+fHwEbuwn5AAAIABJREFU93E7Hrdt0zZVVYoq4H1KDx48uPnOzXv37q3Xq5SzijALiIuimIwnRVnIgAjERADhnCNnS30CwEwhhKqqJEQmMIuolEXRNE2MEQMiJgoh1nWtoua57/rVapPNJKiKEouysDAxM/E5kaKITd2M2mY8bquyYpGc0nK++PLZk3v37p4cPTczDaFpRpevXPnOd7517dpL09nsjX98gq3P/uWBKv+937ye+v6D6+9h69WffgtAAixb31uX+9TnlJ1E67Ksm9G4mdR1GTSKsLAIS4haV1U7Gs1m0/29vd3dvd3dWV1WGFiGZQJUBUCf+sGm780MRCACQCoaYlVVTduGGM3ZPKecV6v1yenZJ5/evfXee3fv3js7m2/WXYaHoKNRc+HC/qs3rl+7cuXywd5s3JaFKmCeUt93XQf3oBxENQZhBhwGgHrLfdf3KSf3PluX8mrdnS6Wz0/ODp+ffPns2ZMnh4fPj45OTuaL1abrcsIWAxR+9w5e6P/8NcAAGNzdALg5YBiwqDALsahqICLPTsJFWcYQBymnxdlisViu1qu+74kIgDtA+AoBBAKBwGBiwDBwNwAGx8DxFXeccwDu2CKAiAAinCPAMXARCVrEEGOhRGxubrnvs+XscLNs7jADM2AwONzd3PAVYvwaN7AwB4khhhhZVIQte87Z3P2HH+CFg7u/z6wA3OEOgJjZsnXr3uDMVJTluJlMp7PJbLIzm02ns7ZpQlkEERIQgwEiAgjwbGZucAeImFJKq9VqfjY/Pj6C+7gdj9u2aZuqKkUV8D6lBw8e3Hzn5r1799brVcpZRZgFxEVRTMaToixkQARiIoBwzpGzpT4BYKYQQlVVEiITmEVUyqJomibGiAERE4UQ67pWUfPcd/1qtclmElRFiUVZWJiYmficSFHEpm5GbTMet1VZsUhOaTlffPnsyb17d0+OnpuZhtA0o8tXrnznO9/6h//TdWzduvjW7H/5NMSgyvSf/e0bfd8tl+ucE8D/7xs/xdb1D95IOXV97nPuemeRuiybthmPJ6N6pCqqQURijFVZ1VXVNM1sOtnf3dvd25lNZ0URPWXLmS2LkCjD0Xddl/qu73I2AxwwNyLiEMqqGreTUEQH3JEdq/X69Gz+6d17t2699/Gn906OjpbLjbnFGMfjyaXLl77x6o2Xrl4+2J1N2rqKKgSzbCml3BM8iIagMSgzw83c4dTnvFl3fUrJPZl1yVabbr5YH56c/sn7/x22Xvr4t0+OTxer9aZPKRuMASp/9Am28v/1hpMDDpgD5u5mDgfM3QHQQESVWTVoAJCSqUpRVmVZxBhzn09Oz5aL5WazTikDcPg5fI1ATASAhBmELTN3GBwDdwfgcACOX0EYEAEgEAhb7k5EMZZFiDEGMLnllHPuUzZzmDvMMhxM+IrD3Q3/Hm5uDhYSlVDEGEsJSkTdD97DC3rrb9ZlVZSVaCBiM/NsABNAIpa9W2/MwSJlVU7ayXQ629ndmQ12dsdtG2PUKAyADXAQMTMGDvOM7GBiopTzerVeLOZHx8cp51FZ1aO6GY2qqmRVEWaihw8fvf3OO/c++XS+WllOLEJEcMRYTsbjuq5CiMwCwjmHWc6WU5/7lODGLDHGUdMUMWJAIKKiKMdtUxQFiJgIREVRjNs2hJBz3nT9arnMKYsqawiqLCrMTGQAMwXRoiiqejBqm6aoiqgRQNd1T54++eijnz179jSnDCCEsH9h//XXX3/55Wv7Fy781j9TbH32Lw+U+R/+9qt93928fBNb3/roN7Kbmfe9pZy6Pvc5d72zSF2WTduMx5NRPVIV1SAiMcaqrOqqappmNp3s7+7t7u3MprOiiJ6y5cyWRUiU4ei7rkt913c5mwEOmBsRcQhlVY3bSSiiA+7IjtV6fXo2//TuvVu33vv403snR0fL5cbcYozj8eTS5UvfePXGS1cvH+zOJm1dRRWCWbaUUu4JHkRD0BiUmeFm7nDqc96suz6l5J7MumSrTTdfrA9PTp8+e/7F08Mvvvjy6eHRyfHpYrXe9CllgzFABC5+9HO8kP78NcAcMHc3czhg7g6ABiKqzKpBA4CUTFWKsirLIsaY+3xyerZcLDebdUoZgMPP4WsEYiIAJMwgbJm5w+AYuDsAhwNw/ArCgAgAgUDYcnciirEsQowxgMktp5xzn7KZw9xhluFgwlcc7m7493Bzc7CQqIQixlhKUCKybH1Kbs6/9VO8cPHeHxCxmXk2gAkgEcverTfmYJGyKiftZDqd7ezuzAY7u+O2jTFqFAbABjiImBkDh3lGdjAxUcp5vVovFvOj4+OU86is6lHdjEZVVbKqCDPRw4eP3n7nnXuffDpfrSwnFiEiOGIsJ+NxXVchRGYB4ZzDLGfLqc99SnBjlhjjqGmKGDEgEFFRlOO2KYoCREwEoqIoxm0bQsg5b7p+tVzmlEWVNQRVFhVmJjKAmYJoURRVPRi1TVNURdQIoOu6J0+ffPTRz549e5pTBhBC2L+w//rrr/+T//2H2Lp18a3Z//xpKIIy0z/6D9/ou26+WPY5MeiDVz7A1is//WbX912XNn3uk5PoqCrHbTvbmdX1SEViLMqybJpm3I7Hbds0o7Ztxm07GtS1iFjXE7yMGpSFxWDW585S7rtkOZlbtmTZ4MwSYqxHIw0RBGJhCX3K88Xi7t37b797886dj589ez6fLwxelNXOdPfqS1e/+cY3Xrp8cTptxnURg6iQwAkOgJmiahBmInfPltyciHL2LqW+z9mty7lLttx0i+X68Pjsf/zxP8PWtY9/++R0vlhucraczTLcEH70c2zZn7+Bc+5whzncYD4wwzl3DIgEyiIhgMiyi4TRqC6Ksohlzvns7Gy5WG76TU554HBzd4BwzgECCREIYCYidhjgcJjB4TjncHI44HD8KsI5AhEIWw4noqAxSNAQmJEt5ZxTn8zNzQ3mDgJUmUDYcji+Zvgl7nACMbNIiCGGyKLE1L/5b/GCvPtmUVZFWcQQidkdni1lB0AgB3KfATBLjGU7bmaT2e7e7u7e3u7ufjtuyyIyCzjDsnnCgJhBYMA8m5k7E8zdM1LuN92m23Q5ZRZpRnVRlWGgYfDFF49//OP37t29O5+fdV1HLNgKGsqyrOqqquoiRBYBvBv0fdd1fden1ANQDUVR1HWtIbhlACJaVWXbtmVZEhEzi0hRFPVopCIp577vN+tNzhkOEY1FGYsYQxQRDAhMHEKoiqoeVfWoqeqqipFUPOUvnz69/eHtL7943G36lDIx2ra5cHBw+dLlqy9d/Uf/22vYevR/XCSif/x73+q77v/e+0tsfffj7xmsz5b63PV916VNn/vkJDqqynHbznZmdT1SkRiLsiybphm343HbNs2obZtx244GdS0i1vUEL6MGZWExmPW5s5T7LllO5pYtWTY4s4QY69FIQwSBWFhCn/J8sbh79/7b7968c+fjZ8+ez+cLgxdltTPdvfrS1W++8Y2XLl+cTptxXcQgKiRwggNgpqgahJnI3bMlNyeinL1Lqe9zduty7pItN91iuT48Pnvy9PnnXz79/PMvnh0enpzOF8tNzpazWYYbCKQ/uoMX7M9fd7jDHG4wH5jhnDsGRAJlkRBAZNlFwmhUF0VZxDLnfHZ2tlwsN/0mpzxwuLk7QDjnAIGECAQwExE7DHA4zOBwnHM4ORxwOH4V4RyBCIQthxNR0BgkaAjMyJZyzqlP5ubmBnMHAapMIGw5HF8z/BJ3OIGYWSTEEENkUWJys65LMOffvo0XDj79fWJ2h2dL2QEQyIHcZwDMEmPZjpvZZLa7t7u7t7e7u9+O27KIzALOsGyeMCBmEBgwz2bmzgRz94yU+0236TZdTplFmlFdVGUYaBh88cXjH//4vXt3787nZ13XEQu2goayLKu6qqq6CJFFAO8Gfd91Xd/1KfUAVENR/H9UwVuMZWmWH/T/Wuv7vr33OfucOHGPyMjMvlXfxj0M3VMzPfYMjCnGkkHIQkaALK7iIhAGHjAP4JGAEcYW88BYHiHeQEjIICSeECNL7ikx7e7q6q7qqW7T3ZVZlbeoyspb5C0izjn77P19ay1ORFfO2L9fNRqNQoxuCkAkNE09mUzquiYiZhaRqqpG43EQKao5537VqyocIiFVdapSiklEsEZg4hhjUzWjcTMat82oaVKiIF708cnJT3/208ePHg59LkWJMZm0e/v7f+edfw2X3j14fe/3Pxa69G/9+V8Y8rBYLIacCfSjz/0Il177yS/0Oa/6Vd9bVrCEdtxMZ9Odra2mGRFTlerpdGNzNtvd3dmczdq2HY2aJlUhxRgCAVYKA02VYhQhrDlMzd20qGbVUkrWYmYkzBJiVQUJIGaWVDdqtuy6O7fvvfX2D96/8cHjxyfzxVyBcT3e2d37zPVrX/nKl4+O9qdtPa5jJRwDCRMzR+HAElMUwFxN17K7E7EDRS0Xy1r6rEPR5WqYL1dPX5z+d+/+NVy6+uGvn50ulsPg5kXdsq+Fv/ABLtm3vgI4GIA71AE3N5hD4XAABjDWiEQiA2SKGMOoGdd1k2LKqufn513X5ZJLUVc1wNfguGQAg4SIABCDAcOaw9zxc441h2PN4QAIa+QACAQQQCA4CHBcIEKUJCEEFjCsaNFiqurq+BQBEphB+DnGpwwOBwx/isEgIgQJEkKMQQIRldffwyv8/a+nqkqpCjEFEgAOlKIwgBlrBoCIOaY0Ho1ns9n+3v7O7tredLIRk4iQoxQteRgAA5iYhEnd85BVFW4gChJFGKCiZbXsANSjOqUUY6piiik9efL4vffeu3vn+OzsRbdaAYxLQaSq6rppxuO2qhKTmGvfD3kY+jxoLqoFQAgxpNDUNbFoUSZUVdWMxxuTSV3XRCRrIVRVapqRCOdSSi5aci5qxVikaZq6aZqq4hDgcHOHi3CVUtM0o7ZtR6O6aUKIAJ4+Oblx88aDhw+7bpmHwdxDSO242d7ZuXr12n/5f/8zuHT8v+8S0b/3L/zSkIf/d/OPcOmX7n5d3VU199rnvOpXfW9ZwRLacTOdTXe2tppmRExVqqfTjc3ZbHd3Z3M2a9t2NGqaVIUUYwgEWCkMNFWKUYSw5jA1d9OimlVLKVmLmZEwS4hVFSSAmFlS3ajZsuvu3L731ts/eP/GB48fn8wXcwXG9Xhnd+8z16995StfPjran7b1uI6VcAwkTMwchQNLTFEAczVdy+5OxA4UtVwsa+mzDkWXq2G+XD19cfr45NmDR08ePHz89Nnzs9PFchjcvKhb9jUikt+6iVfszS8D7lAH3NxgDoXDARjAWCMSiQyQKWIMo2Zc102KKauen593XZdLLkVd1QBfg+OSAQwSIgJADAYMaw5zx8851hyONYcDIKyRAyAQQACB4CDAcYEIUZKEEFjAsKJFi6mqq+NTBEhgBuHnGJ8yOBww/CkGg4gQJEgIMQYJROTuORc3l9/4KV7Z+vAvBBIADpSiMIAZawaAiDmmNB6NZ7PZ/t7+zu7a3nSyEZOIkKMULXkYAAOYmIRJ3fOQVRVuIAoSRRigomW17ADUozqlFGOqYoopPXny+L333rt75/js7EW3WgGMS0Gkquq6acbjtqoSk5hr3w95GPo8aC6qBUAIMaTQ1DWxaFEmVFXVjMcbk0ld10QkayFUVWqakQjnUkouWnIuasVYpGmaummaquIQ4HBzh4twlVLTNKO2bUejumlCiACePjm5cfPGg4cPu26Zh8HcQ0jtuPk/Pv7PcOndg9e3fu+egC786//0V/MwnC3mOWdi/snnf4xLr/3ka/0wLFd9n7VklxjbyWg2nW5tb42axh11XW/Ptnb3d48Oj3Z3t9u2baqKmIkAB2BuHghRKAgDRsSBBYI1U+2L5pKLFoeDCSxMApCDQ5Sqbsyx7Fa3bt/97ltvv//+B48eP5kvFgC143Z3b/8z1z/71a9+6ejK3sa4qqsQhZIwC8UgKYQYQmQBXHUopeRSAI8hApTViuow6KqUfsiLrj+bL5+9PP3vf/xf4NL+z371fNH1fQHIHJrNzeW3buKSvflVwAEH3OGAOeBQA2AOEDMDZKZgMIsBVjwEaepRqqoYUi7l7Ox8uepM1dxwyQGDwx2vMJgIf8IBg2PN8YrjUwSAsUYgrBFAeMWxRgARB1kLQgxAYa6qRQ0OGAAmJgYRCIQ1BhETyOHuBgPgDgNAYDCBAGaASCRK5CBM7N/8EV7xt//JGGKMIcYkHEEEMGDuWCMwk7AIARxiUzebs62Dw4OD/f3d/f1pO4lRQK6Wcx5Wq05VAeI1IVPtuj4PQ84DmOtUN01dNzUxD/0AIKYYJKzFmKoqnTw9+fF7P7pz9+7z58+Xy4UrHCBGDLG+0IxGoxAjQKql7wdVdTMABAZjjYiYGUDJOUhs29FkMp1ubNR1ApGIpJhSlZq6BnEehqLFzYtqHjKLjEbjcdNUdS0SzM1UtRQQJISmbtq2HbftuBnFFB04ffHi7t17jx8/Pp+fd6vOVGGgIJO23d3b+/13/gouvf8/T4nlr/7lX87D8O2db+PSL975ugGmlnvth2G56vusJbvE2E5Gs+l0a3tr1DTuqOt6e7a1u797dHi0u7vdtm1TVcRMBDgAc/NAiEJBGDAiDiwQrJlqXzSXXLQ4HExgYRKAHByiVHVjjmW3unX77nffevv99z949PjJfLEAqB23u3v7n7n+2a9+9UtHV/Y2xlVdhSiUhFkoBkkhxBAiC+CqQykllwJ4DBGgrFZUh0FXpfRDXnT92Xz57OXpg8cnjx6f3H/0+PmzF+eLru8LQObQbG4OkPzWDbxib34ZcIcD5oBDDYA5QMwMkJmCwSwGWPEQpKlHqapiSLmUs7Pz5aozVXPDJQcMDne8wmAi/AkHDI41xyuOTxEAxhqBsEYA4RXHGgFEHGQtCDEAhbmqFjU4YACYmBhEIBDWGERMIIe7GwyAOwwAgcEEApgBIpEokYMwMeDqBnP+cz/BK9Of/aZwBBHAgLljjcBMwiIEcIhN3WzOtg4ODw7293f396ftJEYBuVrOeVitOlUFiNeETLXr+jwMOQ9grlPdNHXd1MQ89AOAmGKQsBZjqqp08vTkx+/96M7du8+fP18uF65wgBgxxPpCMxqNQowAqZa+H1TVzQAQGIw1ImJmACXnILFtR5PJdLqxUdcJRCKSYkpVauoaxHkYihY3L6p5yCwyGo3HTVPVtUgwN1PVUkCQEJq6adt23LbjZhRTdOD0xYu7d+89fvz4fH7erTpThYGC/D/PfhuX3j14Pf7OT1OMxEJ/5de/nPNwtliUIbPIT1/7MS699rNf6vth0XX9UHLxGONkOt7cmG5v71R17WZ13ezu7Fw5OLh29drO7s5k0lYpwQ3q6gpACAwjOAPmyoQYkwQRIXUfhly0ZFWsEYFZDQ64g0Oo61oN82V3+8O7/+Ctt27e+PDJydPlshOW0WSyv3tw7fq1r3zptaPDvY1JPUoxRgpCzBSYYxAWiUxmnkuvpZgqMaeUQKyqueiQdTWU5ao/X3bPX86fvnj5P976HVza/PE35l1fihLYHarmBnnjBi7Zm18FHHDAHQ6YY80dBoDAxALAzAAQs8NKQQxS101MlUgsOZ8vFt1q5VBT+BrMsOZwrBnAWCMmGF5xAG4A44LBATAIf4oYMALjAoEBw5o7AAYRcSBhIWIhIl8zN1V3A4EYRCDA4QAIRMIEYoYZHO5uMHcYgcFEBGJxwAEwB4kiQiL0zR/hFX/76yIsEqOEECNzIGJiAmDqRJRiRSwAhCRVaXNz68rR0cHBwf7e/mTaMjNgaiUPfdctc8kACV/IpXTdsutWfb9ioqpu2vF43LYhhFKKmwdhEgnCIcS6bp4+e/rTn/zk7t27T56cLBcLAMQkIaaYmqau1lLNLKpaSu6HAe5EEkRYyNxLVjM1U3O4alU3m7ONjc3ZbGMjVRWAIBJTqi4B6PteVQGYWRmUgzRNU9d1VVVMXLRoKf0wwJ1FqqqaTibjtm1H46ppgshyuXj0+MnJyZNnT5+enZ113SqXDKCq6tnG9O/e/U9w6b3/qW7q5j/8F7+e8/Ddve/g0tfufAPEqpYH7fth0XX9UHLxGONkOt7cmG5v71R17WZ13ezu7Fw5OLh29drO7s5k0lYpwQ3q6gpACAwjOAPmyoQYkwQRIXUfhly0ZFWsEYFZDQ64g0Oo61oN82V3+8O7/+Ctt27e+PDJydPlshOW0WSyv3tw7fq1r3zptaPDvY1JPUoxRgpCzBSYYxAWiUxmnkuvpZgqMaeUQKyqueiQdTWU5ao/X3bPX86fvnj58PHJ4ycnj548ff7ydN71pSiB3aFqbliTN27gFXvzy4A7HDDHmjsMAIGJBYCZASBmh5WCGKSum5gqkVhyPl8sutXKoabwNZhhzeFYM4CxRkwwvOIA3ADGBYMDYBD+FDFgBMYFAgOGNXcADCLiQMJCxEJEvmZuqu4GAjGIQIDDARCIhAnEDDM43N1g7jACg4kIxOKAA2AOEkWERBgwuDv4136MV0Y//o0QI3MgYmICYOpElGJFLACEJFVpc3PrytHRwcHB/t7+ZNoyM2BqJQ991y1zyQAJX8ildN2y61Z9v2Kiqm7a8XjctiGEUoqbB2ESCcIhxLpunj57+tOf/OTu3btPnpwsFwsAxCQhppiapq7WUs0sqlpK7ocB7kQSRFjI3EtWMzVTc7hqVTebs42NzdlsYyNVFYAgElOqLgHo+15VAZhZGZSDNE1T13VVVUxctGgp/TDAnUWqqppOJuO2bUfjqmmCyHK5ePT4ycnJk2dPn56dnXXdKpcM4A8X/y0uvXvw+tl//J3N7VlTN/Sv/vqX+2GYny+KliDhp6+9h0tfuvmN1WpYdMtVn3P2GMN02s5ms+3t7aaq1UpdNwe7uwcHh1evXt3Z2Z6M2yoJ1gzmyoQYhYGSB9XBSgajinVMIcZobkMuRdXNjFmYFNCi6u4glhhDympn5/Nbt+5857vfu/nBrRfPXvY5x1hNJu3Wzt7Vo6PXPv+ZKwd727Nx26SYOAgRwHCCAyDAdC2bGgCOoapqZs5Fc9G+6GqV58vuxdn85PnLJ0+f/92nfxuXxu/+E6tVzmoAYc0BA79xA5fsza84DHBcMMcldlwiEJEAMHcARAzA3EVCXTcxpSApqy4Wy2EYnFxVh9VQLBvWnEEAjMAAgwECOdacADeYAewwrDkABmGNcMFxgfCPcWc4gQjEBIGwCBET0xoAUwPM4QSQEADT4m4sQsTChEtmMFeYO4zAJES8JgZoMTCLhBBjEPFvvodX6PvfIDALx5hEYghBggSJAPKgxJxSChLdjUlCDLPZ1vXr1w4OD/d2d0fjFjDAQdBhmHfzUgaAmJiFSynLxaLrumXXwb2q69FoNB6PYwi5FDMDwExEnFKqq/r07OX77988Pr538uTJYrEMQWKIdV1XdVOlKqbIJOaWh7WiWgCEGEQEgKquVv0w9EM/AIgxtm27t7u7tbO9tbkZU1JVIY5VqlLVNDWArlupFlwypyBSVVUMkUUcPqz1w5AHM2Pmuq7bS5O2HY3b0aiBYz6fnzw9+eT+/UePHj179rzvVxJijKGq6j94/tu49IO/Izs72//2P/e1fhi+v/8dXPra3V8RFnUb+rJaDYtuuepzzh5jmE7b2Wy2vb3dVLVaqevmYHf34ODw6tWrOzvbk3FbJcGawVyZEKMwUPKgOljJYFSxjinEGM1tyKWoupkxC5MCWlTdHcQSY0hZ7ex8fuvWne9893s3P7j14tnLPucYq8mk3drZu3p09NrnP3PlYG97Nm6bFBMHIQIYTnAABJiuZVMDwDFUVc3MuWgu2hddrfJ82b04m588f/nk6fPHT04eP3327MXp2flitcpZDSCsOWBY4zdu4BV984u4YI5L7LhEICIBYO4AiBiAuYuEum5iSkFSVl0slsMwOLmqDquhWDasOYMAGIEBBgMEcqw5AW4wA9hhWHMADMIa4YLjAuEf485wAhGICQJhESImpjUApgaYwwkgIQCmxd1YhIiFCZfMYK4wdxiBSYh4TQzQYmAWCSHGIELMuGS/8kO80vz4N0RiCEGCBIkA8qDEnFIKEt2NSUIMs9nW9evXDg4P93Z3R+MWMMBB0GGYd/NSBoCYmIVLKcvFouu6ZdfBvarr0Wg0Ho9jCLkUMwPATEScUqqr+vTs5fvv3zw+vnfy5MlisQxBYoh1XVd1U6Uqpsgk5paHtaJaAIQYRASAqq5W/TD0Qz8AiDG2bbu3u7u1s721uRlTUlUhjlWqUtU0NYCuW6kWXDKnIFJVVQyRRRw+rPXDkAczY+a6rttLk7YdjdvRqIFjPp+fPD355P79R48ePXv2vO9XEuK3h7+FS+8evH7jX/o/v/TlL+7sbNNf/rUvDcOwXCxy1hjj+1/8Y1z60s3XV6v+fLHsh2HIHiVMp+PZbLq7u9PUIzUbjZqD/YMrB/tXDq9sbW2Om1GVAggMmHlgSnUUIA9dyas+9wBiTFVMMQU35FKKqWGNJIi6D0XdDMxMQUIcsr48Pbv1wZ3vfO/tW7funJ6eqVpVN+14OtvePDw4+Nxnrl853N3enGyM67qWIAR3M9M8mKqbqZmbAk4sKcWqboglF81Fc7Hlqn95Nn/64uXDJ88ePTn5g/y/4FL9/a8NWYuaA3CQEQB+4wYu6Ztfdhhg+Ecx1ogAMIEBOBwgYgag6iKSUpOqikOwoovlSnMBI2tZrbpSisHwJwhrDGHACBccawYFwGAABgPAYFwwXGBcMAMYjAuGNXcBmIjABBJiMDExCwPkbm7qcAAsBEC1wMFCRCIMgAEzg7m6OWAEJiFiWXNHKeosIiHEGCT4N3+IV/id1wlMJCmmGFNIMYSYgphj1Q8MqqpaQnQ1MMcQZptb169fOzg43N3erpoGpoABGMqwXC5yHgDwmojZpUROAAAgAElEQVRpWS67brXq+xUcMaWmruqqYRHVYmZwBxEzx5Sapjk7Pbt168N7xx89evhw1XUSYlPX7aStU8USWQhAKZqHQbWoQZhDCCziriWXZbca+lXJ2YAYZDKdHR0d7u/tb25upirlPgOIVapS1TQ1gGXXlZLdnYhDiFECBxFmAKraDxdyn9VNRKqqatvx2mg0btt2Otmo6uRmL09fHt/76P79j+/fvz+fz2OIEkVY3uz+Bi59628sj64c/jt/6RvDMPzwyndx6c/c+VUWccfQ59WqP18s+2EYskcJ0+l4Npvu7u409UjNRqPmYP/gysH+lcMrW1ub42ZUpQACA2YemFIdBchDV/Kqzz2AGFMVU0zBDbmUYmpYIwmi7kNRNwMzU5AQh6wvT89ufXDnO997+9atO6enZ6pW1U07ns62Nw8PDj73metXDne3Nycb47quJQjB3cw0D6bqZmrmpoATS0qxqhtiyUVz0Vxsuepfns2fvnj58MmzR09OHj95+vzF6en5Yrnqh6xFzQE4yAiX+I0beKW8+QX8oxhrRACYwAAcDhAxA1B1EUmpSVXFIVjRxXKluYCRtaxWXSnFYPgThDWGMGCEC441gwJgMACDAWAwLhguMC6YAQzGBcOauwBMRGACCTGYmJiFAXI3N3U4ABYCoFrgYCEiEQbAgJnBXN0cMAKTELGsuaMUdRaREGIMEliYAAf09XfwyujHvxljCimGEFMQc6z6gUFVVUuIrgbmGMJsc+v69WsHB4e729tV08AUMABDGZbLRc4DAF4TMS3LZdetVn2/giOm1NRVXTUsolrMDO4gYuaYUtM0Z6dnt259eO/4o0cPH666TkJs6rqdtHWqWCILAShF8zCoFjUIcwiBRdy15LLsVkO/KjkbEINMprOjo8P9vf3Nzc1UpdxnALFKVaqapgaw7LpSsrsTcQgxSuAgwgxAVfvhQu6zuolIVVVtO14bjcZt204nG1Wd3Ozl6cvjex/dv//x/fv35/N5DPE79ru49O7B62/+2d/7xi//8tGVQ/pLv/zaMAyLxVLNQogffuWPcem1m693Xb+YL5erIRcXkXZcb86me3u77XjswHTcHh4dXjk42N/d25huNE0dk0QWEGAWRJo6MUNLn4dV33dqKhxCDDGKO4as5uYAMYPFHathcCDGRByEpRvK2cuzD27d/t5bP7h9+8582al6U4/Gk3Y23Tw42L9+/ehgb2d3e7IxGY1GMQZ281Ly0HX90FspZkYgEo4xxFg1TQWWrFbUcrZ5t3r28vTR46f3Hzx68Ojxd0b/Fy6l739N1bOZFXNzBgEkb9zApfzml2CKn2OsES4QwwkEIWasmYMJxK7IqsSSUhVjFUJQ1VW3yllJSLWshlVWBdQAdsYaGcAAMWBYc6w5DMYEhmCNcMFxgXDBcYFwiRlrZu6AMRBICAyYO9aEWGIgIndbUy0AgggAcwPAxCTEDAYbzAE3dXUHiIkIxBJEHFTUAQ4xigQR8W++i1f4+7/CIiGEKtVVqlOdYkjMnFVXi5UDdV2HENSciGJIW5tbV69f3d/bn23MqjqZ6Zq55qHvumXOgzmYSUTcfchZcy6lgCiEGGMIMRJItZgZ3EEkIimlUTM6Oz+7dfv28fHxg08eLBeLENN41Ew3ZikmVXV3AGpmqm5gAYvEkAColqHPq2FlqgDMTE0nk+n169cODw63trdTSsOqd5iEWFWpqRvAu67rh8HMmEPTNFECAHNX01JKzlnLmgJgkapKo6Zp6qZqmrZtp9ONtm1HTbXs+ocPH3z80fEHH9568eK5EIOFmb49/E1c+t/+6r1r16/+5//mPzsMw48/8z1c+uqtX2URc+Scu65fzJfL1ZCLi0g7rjdn07293XY8dmA6bg+PDq8cHOzv7m1MN5qmjkkiCwgwCyJNnZihpc/Dqu87NRUOIYYYxR1DVnNzgJjB4o7VMDgQYyIOwtIN5ezl2Qe3bn/vrR/cvn1nvuxUvalH40k7m24eHOxfv350sLezuz3ZmIxGoxgDu3kpeei6fuitFDMjEAnHGGKsmqYCS1YrajnbvFs9e3n66PHT+w8ePXj0+PHJ89Pz+XI1DLrm2cyKuTmDAAJc3riJV/K3voA1xhrhAjGcQBBixpo5mEDsiqxKLClVMVYhBFVddauclYRUy2pYZVVADWBnrJEBDBADhjXHmsNgTGAI1ggXHBcIFxwXCJeYsWbmDhgDgYTAgLljTYglBiJytzXVAiCIADA3AExMQsxgsMEccFNXd4CYiEAsQcRBRR3gEKNIEBFmxgXXX3kHr2z87LeqVKc6xZCYOauuFisH6roOIag5EcWQtja3rl6/ur+3P9uYVXUy0zVzzUPfdcucB3Mwk4i4+5Cz5lxKAVEIMcYQYiSQajEzuINIRFJKo2Z0dn526/bt4+PjB588WC4WIabxqJluzFJMquruANTMVN3AAhaJIQFQLUOfV8PKVAGYmZpOJtPr168dHhxubW+nlIZV7zAJsapSUzeAd13XD4OZMYemaaIEAOaupqWUnLOWNQXAIlWVRk3T1E3VNG3bTqcbbduOmmrZ9Q8fPvj4o+MPPrz14sVzIf6O/S4uvXvw+v969a9985t/9tr1q/TGL1zPOXfLTtVSjB/90j/Epes/+fpqNSy6rl/lYXASGjdpY2Oyt7PTjlti3pptXLt+9crBwe7OzsZkUqULMQoRoBpERk0SIdWSh67rFkULiIRFAgPQYgoHiISZpLjnnJ0oxsQSCaHP5ez07NatO2+//c7tO8fLblnM69S0bTvdmO3v7V27dnSwt7271W5MR5O2TlHgPgxDt5ivVqtcirsHYo4hxphSqlIF5mKe1YZBXy4WJyfPP3n0+KOP7j949ORHe38Pl8LbX3NDNstDcXgAE0jeuIFL/be+gAuOn2MwiBgEdgKBiBlEWHMCkalnVUCqVFd1lWLtsH41FFUwHDDTYupwwOFwg8McDieYKwxwNzd3NwVAYAI5OwACAQRyAhEIILADJMwEAhxwN2VQZIGhaFYtqgpQTEIkcNcLGYCIEMEdRGAWEPgCzMwBN3WHmxMTEYhFWEBUFGAOEkWCCPs338Ur9tbrMYaUqrpu6ktBEjFK1vn5wuFN3UgQcwhJTGlrc+vatavbOztt28YU3a2UknO/Wq26blnyoGZMJCIgMjNcYuYogYMwEQBzhzsAZpYQqpSaupkv5rfv3Dm+d+/4o4/Pz85jDKOmmc1mMaVhKEWLX4CrkXAMIiHGGADkXIahH4bBzJnJVPt+aMfttc9cOzw83NvZTXU1rHp1E+KQYl3V7tZ1XT8MphpCaEbjGKKZFVUtRVWLqpkBYCIWiTFWPxfTaDxuJ+3GZGNjtgHg9OXpJw8+uXHzxpPHj/u+NwczfXv4m7j0e//KO1euHP43/+m/nHO+9eV3cenzP/sGS3D3XMpqNSy6rl/lYXASGjdpY2Oyt7PTjlti3pptXLt+9crBwe7OzsZkUqULMQoRoBpERk0SIdWSh67rFkULiIRFAgPQYgoHiISZpLjnnJ0oxsQSCaHP5ez07NatO2+//c7tO8fLblnM69S0bTvdmO3v7V27dnSwt7271W5MR5O2TlHgPgxDt5ivVqtcirsHYo4hxphSqlIF5mKe1YZBXy4WJyfPP3n0+KOP7j949OTk6cvT5bKoF3c3ZLM8FIcHMIEAlzdu4pX+W5/HzzEYRAwCO4FAxAwirDmByNSzKiBVqqu6SrF2WL8aiioYDphpMXU44HC4wWEOhxPMFQa4m5u7mwIgMIGcHQCBAAI5gQgEENgBEmYCAQ64mzIossBQNKsWVQUoJiESuOuFDEBEiOAOIjALCHwBZuaAm7rDzYmJCMQiLCAqCjAHiSJBhAnkcHOnX/shXtm8+RfrS0ESMUrW+fnC4U3dSBBzCElMaWtz69q1q9s7O23bxhTdrZSSc79arbpuWfKgZkwkIiAyM1xi5iiBgzARAHOHOwBmlhCqlJq6mS/mt+/cOb537/ijj8/PzmMMo6aZzWYxpWEoRYtfgKuRcAwiIcYYAORchqEfhsHMmclU+35ox+21z1w7PDzc29lNdTWsenUT4pBiXdXu1nVdPwymGkJoRuMYopkVVS1FVYuqmQFgIhaJMVY/F9NoPG4n7cZkY2O2AeD05eknDz65cfPGk8eP+77/dv5buPTuweu/N/53X//l169cOaTXr27nUlarlanFmF78uQ9wafudr5Wcu1WfcxmKE1GdZDIabW3NRk0bK9nb3v7C5z939ehoe3t7YzKp6liFFFNggpUSmJq6DgFqmvOqWy1yHlwNTCRMRHA4EYHATMTmyKWASEISiSIhD3p2Pr995947P/jh7bvH8/N5UY8xNeN2Nt3cP9i7fv3oYH9ra9JsTEfTSVOlCECHYbFc9H1fSnEgiLCEFIOEGGIEsbn3WZf98OL07OHjk48/eXh8fP/Boye3PvdHuCRvfc1BxWwYspsGDgzIGzdxafWtLwAOGD7FxMQMIiYQQCQMEEC4QKqec+EQxs24GbXj8ZhFhmEwNSewcKwSERxuauZuF7SouaqZFlUzLUVNrZi5qamvgZxABCImEBGTgEmYmUhYSJjWsEamDDCJl7JcdatVNwwD3GMIJOKq5l40O1xIRAgAgUmIwMRghpk54ObuhktETEzE4iA3AjimyByIiX7tXbzS/9EvxZhGTTMataNRU9e1hAQgD8P8fOHwum5iDO4uEqqq2d7aunJ0tLm52dS1xMCEXPJyueiWa4th6IsqgCDCIkQUw6dijMxs7gD4EhEFkZhSVVVNXZ/P5x8dH9+9c/fW7dunL09JqK6bjckkpKRFi6rbmpubkFR1lVJMVQ0g52Gt7wdTZaactesWdTM6Ojo6PDw42N9vmibnbGYAmDnGaGZd1+Wci2qQ0DQjCUFVTdXM1AyA8KeEJcQg4UKMsaqq0Wg0nWxsbm7UdSMiz54//+CDm/c//vjk6dO+H6oq/eHid3Dpr7/xB9vb2//Db//7uZQnr/8Ml/be+QUOwYFSrOTcrfqcy1CciOokk9Foa2s2atpYyd729hc+/7mrR0fb29sbk0lVxyqkmAITrJTA1NR1CFDTnFfdapHz4GpgImEigsOJCARmIjZHLgVEEpJIFAl50LPz+e079975wQ9v3z2en8+LeoypGbez6eb+wd7160cH+1tbk2ZjOppOmipFADoMi+Wi7/tSigNBhCWkGCTEECOIzb3PuuyHF6dnDx+ffPzJw+Pj+w8ePXn28mzR9QZykIOK2TBkNw0cGHB4eOMDvLL61mfxKSYmZhAxgQAiYYAAwgVS9ZwLhzBuxs2oHY/HLDIMg6k5gYVjlYjgcFMzd7ugRc1VzbSommkpamrFzE1NfQ3kBCIQMYGImARMwsxEwkLCtIY1MmWASbyU5apbrbphGOAeQyARVzX3otnhQiJCAAhMQgQmBjPMzAE3dzdcImJiIhYHuRHAMUXmQExuMNO16jf/IV7ZuvnPj0ZNXdcSEoA8DPPzhcPruokxuLtIqKpme2vrytHR5uZmU9cSAxNyycvloluuLYahL6oAggiLEFEMn4oxMrO5A+BLRBREYkpVVTV1fT6ff3R8fPfO3Vu3b5++PCWhum42JpOQkhYtqm5rbm5CUtVVSjFVNYCch7W+H0yVmXLWrlvUzejo6Ojw8OBgf79pmpyzmQFg5hijmXVdl3MuqkFC04wkBFU1VTNTMwDCnxKWEIOECzHGqqpGo9F0srG5uVHXjYg8e/78gw9u3v/445OnT/9w8Tu49O7B678b/43XvvjF7e1t+vrRjhbt+17dUkwvfu19XNp995eK6pBzLqUvzkDF1NR1OxnXdZ1S2N/d++IXP3/96tXdne3pZFKnS1VgwPLAzOOm4sBWci79qlsMQ1+suIOYRZhFmBjCADnYHdkMoCCBJAYKuej5Ynn37kc/fPe9O/eOz8/mOavE2NSjjels7+Dgs589Otjbmk3qjbaeTJqUEhO05K7r8jAUcwCBWURY1gKHALDBl32eL5Ynz17cf/jo4/sPjz/65MnTZ/e/8j1cou/8GYDVrc8FsAAmRvjzN3Gp+9bn4YafIxBALMwgEgLABCMwHAQwQG4oalVdz6az2Wx7a2e7qWpVU7gDIUqqKxFWVzczdS1aNKtqUTXVXIqWUnLOqmaW8zD02UwdIICwxhAQSFiYmAIzURAhsAgYIAfMCOiH1dnZ6fx8Pl8uXC1EYRK4q1kpAwhBIjMRAALAxEQA2GFwmMNhAAxgMBHWxEFuIA4xJQmBifybP8Aryzd/MaVU103btnUzaupaJMAx9MP5YgHHqBlJCgBSTOPRaLa5dXh4uLExDSHGGEIMlst8eb5YzJeLRd/3WRVADBIkSJAUU6pSTCnGyMxufgFOREEkxlhVdTNqRk2zWC4/+fj+3Xv3Prx16/mzZwCqqp5OJzFV7nBTUytmrkbMKYaYUooJwKpfDX2/7LpcMgyq2vWrpqr39vb3D/cODw7acQvAzIopHMykqt1qpbmoW5RQ1TWxuJq5uTuBmFmERYTlArOIcBBhkaqqxuN2MmmnGxvTdjIaj7vl4u6947v37h7fPZ4v5k3T/P3z/wqX/vobfzCdbvzt//o/0qInv/ITXNp99xeZBSAzK6pDzrmUvjgDFVNT1+1kXNd1SmF/d++LX/z89atXd3e2p5NJnS5VgQHLAzOPm4oDW8m59KtuMQx9seIOYhZhFmFiCAPkYHdkM4CCBJIYKOSi54vl3bsf/fDd9+7cOz4/m+esEmNTjzams72Dg89+9uhgb2s2qTfaejJpUkpM0JK7rsvDUMwBBGYRYVkLHALABl/2eb5Ynjx7cf/ho4/vPzz+6JMnT589P533Q3ZiBwGsbn0ugAUwMdwsvvEhXun+/mexRiCAWJhBJASACUZgOAhggNxQ1Kq6nk1ns9n21s52U9WqpnAHQpRUVyKsrm5m6lq0aFbVomqquRQtpeScVc0s52Hos5k6QABhjSEgkLAwMQVmoiBCYBEwQA6YEdAPq7Oz0/n5fL5cuFqIwiRwV7NSBhCCRGYiAASAiYkAsMPgMIfDABjAYCKsiYPcQBxiShICE6l7ztmyNm/8f3hlduMv1s2oqWuRAMfQD+eLBRyjZiQpAEgxjUej2ebW4eHhxsY0hBhjCDFYLvPl+WIxXy4Wfd9nVQAxSJAgQVJMqUoxpRgjM7v5BTgRBZEYY1XVzagZNc1iufzk4/t379378Nat58+eAaiqejqdxFS5w01NrZi5GjGnGGJKKSYAq3419P2y63LJMKhq16+aqt7b298/3Ds8OGjHLQAzK6ZwMJOqdquV5qJuUUJV18Tiaubm7gRiZhEWEZYLzCLCQYRFqqoaj9vJpJ1ubEzbyWg87paLu/eO7967e3z3+O+d/jYuvXvw+u9v/AdHR0fT6Qb9U1/+Qtbc99ndY5D7X/sBLl3/2W+4aXHXUvohu3sMEkNIKUS5sLe7/ZUvffH6tas7O9sb00lTrcUqJTjKMARBO25YKOe+H1bDsBpyn4fBzIU5xJCqKoQAZjjUYA4HQGsBJESSiy4X3fHx/T/+4x8dH398Nl8MgzJLXdeTdrq7v/e5z1492N/enDbTcT0apRgCMUwt58FM4QQCEdMaiIVZAkDZsexWz1+cPjp5enz/wf0HDx88ePzsxcsnv/ju/88evMZsu6Z3Qf8fx3Ge57W5r/vZvpvn3a3dzKyZ0koRVz8gbdoPImpixALxA4bEgDYBozU0SsRUQgoSRESq8YMRq8UEBAmU6AdaJtRS7WZROzPtTGez9u/+fZ/9fd/XdZ3neRyH9/NMF61NSAiRRJL1++Ga/dS3gsjUqxvMhUHw8D1fxbXxJ16DA3AQ4RqziDCBwWTmVQ0wgImFSBhMIsvlzp2ju3fv3Lv/4MHu3j4zg9gBEgrCEJhqdXM1Uy1WTVWratWsRUutRYuqaZnnebMe55xxxeAwOK4xCMQkEBYiEUCECGAA7qa6Wq1evnx+fHJycXZeagkhMBjsZla1EDjFJEKq5nDCFoEBOK64m+E3EgBmYI6p7WKMLFx/60/jYxc/8W0ppq5tusWiadoUA1EEUEoZ1yOYFl2fmkTETdMMw7B/cHD71q3FcmDmJsau6wy2Xq8269VqvZ7n2VRBCCHFKDHEmFKTUmzSVpQAoNQ6jqO5xRCbpln0fT8MQ9/POT9/9uzDjz78xtffOTk9dkeKaRiGtulCDESs12otZg6AQSKiquM8bzbr1Wo1TZNec6Bt2/29vZuHN+7cv7u3u5dCBJBLrrWqaillzllViSiEmGIkZjcDQFvMIQQWFhHeIhZmFmFhEem6bm93dzEMXdcNi2FnZ7fU/OLZi3fff/fLv/zl89Pztu/+zuoHce2/+Dd+fjks/8wf/XeKlg8+93/h2itf/m2A4JqbVnetdc7F3WOQGEJKIcqVWzcPP/fmZ155cP/GjcPdnWXXbMUmJThqzkEwLDoWKmWe85TzlMtccjZzYQ4xpKYJIYAZDjWYwwHQVgAJkZSqm/X4wQcPf+EXfvGDDz66WK1zVmZp23Y57Ny8fev11+4f3T7c3+l2Fm3fpxgCMUytlGymcAKBiGkLxMIsAaDi2IzTyen50xcvP3j4+OHjJ48fPzs+PbtYj+OsYHIwiEy9usFcGAR3ePyer+Fj44+/CiJcYxYRJjCYzLyqAQYwsRAJg0lkudy5c3T37p179x882N3bZ2YQO0BCQRgCU61urmaqxaqpalWtmrVoqbVoUTUt8zxv1uOcM64YHAbHNQaBmATCQiQCiBABDMDdVFer1cuXz49PTi7OzkstIQQGg93MqhYCp5hESNUcTtgiMADHFXcz/EYCwAzMMbVdjJGFTW2ec6ml/54v4mP9L/0LTdOmGIgigFLKuB7BtOj61CQibppmGIb9g4Pbt24tlgMzNzF2XWew9Xq1Wa9W6/U8z6YKQggpRokhxpSalGKTtqIEAKXWcRzNLYbYNM2i7/thGPp+zvn5s2cffvThN77+zsnpsTtSTMMwtE0XYiBivVZrMXMADBIRVR3nebNZr1araZr0mgNt2+7v7d08vHHn/t293b0UIoBccq1VVUspc86qSkQhxBQjMbsZANpiDiGwsIjwFrEwswgLi0jXdXu7u4th6LpuWAw7O7ul5hfPXrz7/rtf/uUv/+/HfxTX3j5666+8+Z8e3TlaDkv6vd/zXUV1nrObSZBfuPE3cO3bn/0uwM1Ra8nTZOoiwkJEYALcDw/23/z0Gw/u37t1eLi7O6SmaVNMMRJQ8xREljuLIDTOY8lTqXPO8zTPbsYsKcWu7WMTATJHqeoGZyJiYgEEjpx1tR4//OjRF37xSx99+Gi9nuZSmSmmdrFY3Lx585VX7909OjzcG3aGtk3CgQH4likAYiaQAzAADmaRYMSqfrnePHtx/PjJs/cfPnry+Onz4+OT88vT3/ILuKY/+a3OBMABmBMMsPDdX8W18SdeA+DuRIQt3iIhIWYQmXstamYAiIUgLCFI3D84ePXV1954/dOf+vSnb964FVMiFnczchAM6m7mDtvy6upmpqZqVauqml6pVUvN42aspZibmrmaqlWrbu4OuBlhS8BEIEAIQuSmueSL07PHTx4/ffrs+Pgk5zmEwMy4YqqVSZq2ISYt1WAA3B2/xkGOj7kbwDA4yIwkpr5fxNQEkc23fR4fW33+N4uEpklN28SQmIWZYShFN9MoTMNip2kTMzdtt7OzPDg4uHXzZtf3AJom7SyXIKzWq/V6tVmv53k2M2ZOKcVvCjE2qUmpaZoYIxOXWi4uL2utMcau6xb9YhgWi2HQoi+OXzx69Oidd949Pn5p5iHIsBiatomxjSLM4m61VlWtVUspgM85j5txM46ry9U0TapFzYkohtj37cHh4f17927cuNH3PRHN8zxOV0rOc84AYggiMaYozObORLwlEkMgZmEGEQBmDnIthK7rdnf3Fou+SanvF7t7u0FkvVp/+PDhl774hRcvXorI3z7/Y7j2I3/o/eUw/Jc/+O8W1Z/d/au49ltf/i5zwOEOwM1Ra8nTZOoiwkJEYALcDw/23/z0Gw/u37t1eLi7O6SmaVNMMRJQ8xREljuLIDTOY8lTqXPO8zTPbsYsKcWu7WMTATJHqeoGZyJiYgEEjpx1tR4//OjRF37xSx99+Gi9nuZSmSmmdrFY3Lx585VX7909OjzcG3aGtk3CgQH4likAYiaQAzAADmaRYMSqfrnePHtx/PjJs/cfPnry+Onz4+OT88v1lEtRNXKQMwFwAOYEA8zd4/d8DR/b/PirRIQt3iIhIWYQmXstamYAiIUgLCFI3D84ePXV1954/dOf+vSnb964FVMiFnczchAM6m7mDtvy6upmpqZqVauqml6pVUvN42aspZibmrmaqlWrbu4OuBlhS8BEIEAIQuSmueSL07PHTx4/ffrs+Pgk5zmEwMy4YqqVSZq2ISYt1WAA3B2/xkGOj7kbwDA4yIwkpr5fxNQEEVOf8zTNufnOv4+PxV/87hgSszAzDKXoZhqFaVjsNG1i5qbtdnaWBwcHt27e7PoeQNOkneUShNV6tV6vNuv1PM9mxswppfhNIcYmNSk1TRNjZOJSy8XlZa01xth13aJfDMNiMQxa9MXxi0ePHr3zzrvHxy/NPAQZFkPTNjG2UYRZ3K3Wqqq1aikF8DnncTNuxnF1uZqmSbWoORHFEPu+PTg8vH/v3o0bN/q+J6J5nsfpSsl5zhlADEEkxhSF2dyZiLdEYgjELMwgAsDMQa6F0HXd7u7eYtE3KfX9YndvN4isV+sPHz780he/8Jff+0O49vbRWz/+2/7CnTt3l8NA//H3/du11nnK1RSgv375Z3HtXwp/GHAzqOo8jloVjCumplpq3lsOb7z+6t07d24cHuwsF22KKcYYIly1lBTj7t4QgtOWgf8AACAASURBVIzjai6Taik5T9NUTYU5paYfFik1AEx1zrWaEQmJBAkAl2JzLqvV5uFHj770S1959OjxuMlFzcFBYtd3h4cHDx7cuX/n1tGtvWHRBnEi3wJARMwsITBI7ZscxBIiiKrh7GL1+OnTjx49+fCjR0+fvzg5PbtYj2e/5e/jmv7ktzkTERMz4G7VtYbv/hVcG3/iNfx6zCQkLEwCkDuqVjMH4EYAM0uI8cbhzU996jOfefOzn/3ctxzdPmpSy0GqqZqaqboaGa4RAALABDgAN3c3NfMtM3eoqbuZllpKLjmXknOppeRSTVWrmcEc7oAzIRKb6jxtjo+PP3j/w8dPHj97/nzajCEGJiZmAG7Kgbu2ZeJSirm6mcHxaxxwfMzh7oDBwQ4OqR2GZd91IabjN/4WPjb91D8nxBIkhMAsbnC4K0opc84istzZ7ZpWRNq+3dvdOzw8ODw8bNvW3Zu23d/fY6aLy4v16nK13pSS3RBTaLeaRkRijCGEGFPTNlsxhFLrxflF1ZpibJqmXyz6frFcDFXLyenZk8ePvvGNbxwfH1fVINJ2XZOaIDGl1PV9DIGISq3TNM3T1jxem6ZxHOdSirmZaq3qsMBhb3/3wYNXbt2+tb+3xyLjZrO+Nk5TKZUJqWlSamMMQYSusYgwiwiIsOWOLaIYrsSU2rZd9Iu+65s2dX2/v7ff952IPH/+4otf+uKTx49zzj/27Adw7cf+k4vd3Z0f+fN/vNb6o89+CNf+9eGP5FpM3d0AN4OqzuOoVcG4Ymqqpea95fDG66/evXPnxuHBznLRpphijCHCVUtJMe7uDSHIOK7mMqmWkvM0TdVUmFNq+mGRUgPAVOdcqxmRkEiQAHApNueyWm0efvToS7/0lUePHo+bXNQcHCR2fXd4ePDgwZ37d24d3dobFm0QJ/ItAETEzBICg9S+yUEsIYKoGs4uVo+fPv3o0ZMPP3r09PmLk9Ozi/U4zbWoVoUZnImIiRlwt+paHRa/+2v4xD8Wfvs7mcUNDndFKWXOWUSWO7td04pI27d7u3uHhweHh4dt27p707b7+3vMdHF5sV5drtabUrIbYgrtVtOISIwxhBBjatpmK4ZQar04v6haU4xN0/SLRd8vlouhajk5PXvy+NE3vvGN4+PjqhpE2q5rUhMkppS6vo8hEFGpdZqmedqax2vTNI7jXEoxN1OtVR0WOOzt7z548Mqt27f29/ZYZNxs1tfGaSqlMiE1TUptjCGI0DUWEWYRARG23LFFFMOVmFLbtot+0Xd906au7/f39vu+E5Hnz1988Utf/OGf/T249vbRWz/7L/7Ig1de2d3dof/2T/3JWjTnOdfqaj/8K9+Pa7/v6I+rO8xqrdM4aa0OU61WStWqWneGxYP7945u37pxcLAcFm2MKQYhEFCttinu7u6IYLW5yHmqWrdKzu7GLCmlpmtDCOam1XJRA0QCSwwiDipZV5vx7OT84cNHX/mVrz15+nLOtRYDECQ2bXd4sHf33tH9u7fu3b25u9MHdsDc3dyZSJglRiY2NzNXNYAQAsCl2unF+QcfPXr46PHjJ0+fvTg5PT273EwX/+zbuGZ/79vNQcQhBoJXLW5VvvOXcW38iTcAwzcxAAYTUxBhZgHIzKqaqlZzKERi23a3jm5/9jPf8uZnP/e5b/lNR7ePUtsJoahWzdXU3UAAQ3hLiEmYiYWuMBO2zOFuRCxMgFdVLVpqLqXMuZaSSym11FJy2arFajWtMGPAqpYyH798+d5773/w4YePHz66XK+iCDGzsMNdlUUWfUfEtZZaq8Ph5nAABAKBADCuGBymDjd3J4O0qdvd29/d2Vksh6/t/o/42NFH3+uGf8DMVN3USlHVwiLDYtE0LYu0bbezHJbL5TAMKSVzb5tmd2+PmVbry816td5saq0AYkyLvmuaJoQgIswsITQpNe1W526bzSbP2eExxr7vF32/GAZVPT4+fvrkyXvvvX/88mXRSkRN04YgDElNs+gXTZNERM3mK3mexnGcpmmcp3nOOZfsZlVVq6qpG5bL5Suv3D86Orpx40aIcb21Wl1eXk7TbKbM0jRpS0IIEpiZmIVZroGgaqZaVZkohBC3Umqapmvbtmu7rh8Ww9ZyOfSLYbW6+MbXv/Hk6ZPVav2jX/2DuPb5P113d3b/5l/6b2rRP/XzfwDXvu/Tf7aUWmoxVXWHWa11Giet1WGq1UqpWlXrzrB4cP/e0e1bNw4OlsOijTHFIAQCqtU2xd3dHRGsNhc5T1XrVsnZ3ZglpdR0bQjB3LRaLmqASGCJQcRBJetqM56dnD98+Ogrv/K1J09fzrnWYgCCxKbtDg/27t47un/31r27N3d3+sAOmLubOxMJs8TIxOZm5qoGEEIAuFQ7vTj/4KNHDx89fvzk6bMXJ6enZ5ebacpV1QykBnMQcYiB4FWLWzWt8bu/ik/8Y7n9wfcCMDNVN7VSVLWwyLBYNE3LIm3b7SyH5XI5DENKydzbptnd22Om1fpys16tN5taK4AY06LvmqYJIYgIM0sITUpNu9W522azyXN2eIyx7/tF3y+GQVWPj4+fPnny3nvvH798WbQSUdO0IQhDUtMs+kXTJBFRs/lKnqdxHKdpGudpnnPOJbtZVdWqauqG5XL5yiv3j46Obty4EWJcb61Wl5eX0zSbKbM0TdqSEIIEZiZmYZZrIKiaqVZVJgohxK2Umqbp2rbt2q7rh8WwtVwO/WJYrS6+8fVv/OD/9j249vbRW1/53X/t3oP7uzu79Nf++79oVeeS55zLXP7ET/8+XPu+N/+81lpqraWUPJdSTLXkeRpHwFKKO8vh9q1bt24e3jw8GBZDk2IUwEEAM9omLYfOXC8uz6d5o1rNzR3CHGMgFmY2s6JV1dxJJMSmDSEQRzfLpZ6enj9+/PTDjx6+9+6HL09O86yqBoeEuOgW+3u7t+8c3b9369UHt2/s74TIgaBuW6oKUIhJRJjIgarugAPqqKovT07fee/9x0+evjg+PT4+e/Hi+PRyvXnrbVzjn30rlwpQ0zREBC9b9M9/Adc2P/EGMeFXuQMEIpEgQWIUZjeUoptprlpUkVKzv7d37+79Nz/7uc+8+dlPf+ozN27e7toOhHmeqlZsESAgliDEEkOQIFdYRIhYGEQAE2FLmMxh5m6qpm7m7mqm6lpznvM0T+OVucxjyXPNBa5EdHZ6+t6777333jvvv/f+6fm5MG0BUDXVwiLDoucgtZSqCnPAHE7YImJiImICCOYKc3Uzrw5TtF1/8+bto9tbR59f/RA+9nte+XPTnGspqm6uALRqKWqq5mCiGCPLlRRDjEmCEBFAgKeYhmHBTOM8zdM4TVOt1cxCjIu+b5s2NomZzQxACKFt22EYUojqnqdptV6Zed/3i2GxXC5LqS9fPH/y5MkHH350enpSSnHzIMIiIAksYSvGGIRZAJibm9dS5jzPc96M4zzNucyuDsBhVXVYLO/eu3O0dft2TGmz2azX68vLy5yzMBNLDMIizBxEQogiQkwiEiQyI5dScp6m2dxTiilGCSHFGFMaFou9vb1+MTRN6rt+WCyq6fHxyfnp6cXq8j//yX8V1376z3HX9T/9t/9Xq/rv/61/Bdf+o+/4H+acSym1Vq211FpLKXkupZhqyfM0joClFHeWw+1bt27dPLx5eDAshibFKICDAGa0TVoOnbleXJ5P80a1mps7hDnGQCzMbGZFq6q5k0iITRtCII5ulks9PT1//Pjphx89fO/dD1+enOZZVQ0OCXHRLfb3dm/fObp/79arD27f2N8JkQNB3bZUFaAQk4gwkQNV3QEH1FFVX56cvvPe+4+fPH1xfHp8fPbixfHp5XrOuRpiTAbkUgFqmoaI4GVLtYTv+jI+8Y/ld+z+CXMFoFVLUVM1BxPFGFmupBhiTBKEiAACPMU0DAtmGudpnsZpmmqtZhZiXPR927SxScxsZgBCCG3bDsOQQlT3PE2r9crM+75fDIvlcllKffni+ZMnTz748KPT05NSipsHERYBSWAJWzHGIMwCwNzcvJYy53me82Yc52nOZXZ1AA6rqsNieffenaOt27djSpvNZr1eX15e5pyFmVhiEBZh5iASQhQRYhKRIJEZuZSS8zTN5p5STDFKCCnGmNKwWOzt7fWLoWlS3/XDYlFNj49P/r2/9O249vbRW4//wI8fHh52XU9/56//TTMvJc85z3P+D37sd+IT/3Dj598kAQEOuKqZA2AWjiHGGCQQcal1tRpLrUS8WAxHR7dfefDam29+9tXX3nhw/8Hu3kFKiZlyqaoqgYiZBCxXYtiKIQiLCLOIMDMxMxEA2mLGlruZATB3JgJg7qqac56nab3ZTJtxGsdxs5rWa61FQrg4O3333XffeffKyfEpYA53s6paSxGWftGHxK5ubmruMKhhi0FEDCIhAhMDhi0HTEkdi8Xy6Ojuvfv3Xn311f/5a38YH/uT/9rf3WzGOc+1FFU186pactGqgJuZqpk7ExETE5u71upuAIUQuq5j4VrzPM+bzWbOGe4xxmEYur5vUsNMuRQ3I+YmNcNyaFMjQeacz07P3K3v+67vh2Eopbx4/vzZs2dPnjw5OztXrVUVAIFATCAmJiK5wiLCLADMtFad5mma5nme8lzMKjG7Wanad+3t20e3b9+6det20zbjOK7X6816XaoKSwgCAhEBCMIpJZHATCIhxghCKTXP8zhu1D1thShbMTQxDYvh4PBgMQwhhK7rdpc7HGSe82azvry8/CN/5Ttw7ef+67ZJ6Us/83kz//0/8h249p/9zh8rOc95q5SSS87zlVzKVEvN85THGdCmS8MwHO7v3Tw8uHnjcLkcuhSFSLUyIEHaJvRda6qX6/NpmhwGQIRZJMRIBDPTqkWrqhOLhBhTEokwKKxUPT+7ePzoycNHTx4/fnp6dlmy5qJbzNKkZrncuXl4cOf24YMHtw/3l00ThMjhVa8AFFNKsQlBiKi6u0JhU67rcXz+/OU777//7PmLy8v1ydnli+Pji8v1ZsoOSk0DYJoLgZq+iSJwr7XkPKuW8F1fxrX5J990wFXNHACzcAwxxiCBiEutq9VYaiXixWI4Orr9yoPX3nzzs6++9saD+w929w5SSsyUS1VVCUTMJGC5EsNWDEFYRJhFhJmJmYkA0BYzttzNDIC5MxEAc1fVnPM8TevNZtqM0ziOm9W0XmstEsLF2em77777zrtXTo5PAXO4m1XVWoqw9Is+JHZ1c1Nzh0ENWwwiYhAJEZgYMGw5YErqWCyWR0d3792/9+qrr945Otrd2+va9gf+l9+Oaz/w3X+jlqKqZl5VSy5aFXAzUzVzZyJiYmJz11rdDaAQQtd1LFxrnud5s9nMOcM9xjgMQ9f3TWqYKZfiZsTcpGZYDm1qJMic89npmbv1fd/1/TAMpZQXz58/e/bsyZMnZ2fnqrWqAiAQiAnExEQkV1hEmAWAmdaq0zxN0zzPU56LWSVmNytV+669ffvo9u1bt27dbtpmHMf1er1Zr0tVYQlBQCAiAEE4pSQSmEkkxBhBKKXmeR7HjbqnrRBlK4YmpmExHBweLIYhhNB13e5yh4PMc/43/8IDXHv76K2L7/+ZYRialOjnfvzvqnmppZRacv63/qfvwCf+4ezv/TOAO9zUqqtWNShDOIiEGEJkCVZ0tRndvUnt4eHhq6+/9vrrn3r99Tfu3rm3t7fXdQvmIMIgEEuIwkGIIUwhRGHhIEFYRJiZmIWZiEAEQJhZhIjcDIC7AyAibBGZaq015zxNU57zPI+by9XJ8ctp2gC4OD374IP333vvg3fff+fk+LRqsVKLq5ZaSyYJXdumJoowADNXVXdzM4cBIBAJybXAzCLEkSAALXf27t6998orr3zqjTf+7P/xu/Gxv/ofPpmneZzGPOVcs6qaaqmqpcw5z9O83owlZ9VqcCFxdzM1dyaSELuuZWHTOo7jxcXFOM3u1jTN/t7eYhjapgVQSi6lAogp9n2fUhKRWnW9XsG97/uu79u2LaUcv3x5fG212phpVXVTc8AJW76FbyLCN7mjqtZS6pWSS3VVB0oteS6piTcObxzeOLx182bTNPM8b8ZxnuZaNQQB4YqjVg1BmjZuMbFICCkwUa1acpnzBCClJoTAwjGE1LTDYtg/2BsWQ4xpWAx7B7td01XVPOfVevX7/qsHuPaF/24pIbzziz+n5t/7w2/iE//I9O99q6lVV61qUIZwEAkxhMgSrOhqM7p7k9rDw8NXX3/t9dc/9frrb9y9c29vb6/rFsxBhEEglhCFgxBDmEKIwsJBgrCIMDMxCzMRgQiAMLMIEbkZAHcHQETYIjLVWmvOeZqmPOd5HjeXq5Pjl9O0AXBxevbBB++/994H777/zsnxadVipRZXLbWWTBK6tk1NFGEAZq6q7uZmDgNAIBKSa4GZRYgjQQBa7uzdvXvvlVde+dQbb9y7e+/GzZuLxfC9f/o2rr199BY+8f+Ft56+jWtvH71lf+xLTZskBPrCT/28u1YzVS21/t4ffhOf+MQn/n/vl35kX5g++OVfctd/+c88wCf+kdn/+ZvdrKpWrW4GBnPgEAJHFnH3acosslwu796595nPfOb1Nz71yoNXDg9vpKYLIREkRIkxhCgSIwuISZiJJQiLMLMQszDTFhNATACImYWFmNwccAMYW0RMRHD3qlZrLaVo2crTZnN+ejqNG4NdnJ8//ujRw0cPP/zww5PT0zxPc865zPOUS8kAYkwxCosQ4HB3c3N3MwIDwhxCkBhTk9qmSbEJMaWYJDS7y92bt27fvXPn3oP7t2/dPjw46Pr+t38/4RP/ZLx99Fb8oa9JEGGiX/6Z/9sdBjczd1czN63qJZdS8zzO0zxemacy5Xme5nkcx800bWAaYxiG/ubhwc7Ocmi7mARmMUjbtovlsL+7C9jp2VkpOcQAeK2a53G13uSci1ZhTk2SmIIEgN20qqk6i3RdLxLNrFQvRc2MSFQ9z3maSymZgaZNTK55NK0xALBStaqaGgs3TRNjCiE6UKtW1VLt/OLi0eMnz58/f3F6shlnc5pzXa3Xq/X69HOfxyc+8U+Pr/zoAQEPv/or7vgdf/IWPvGPzH7mt8DczIoq4ABIOHBgEYABqFrX9Yc3brxy/8Gb3/K511974+6dezs7O2AhFiGJMbVtkhjAIAYTiIiviTARE9MWExMBIFwjJiEhhhsAxzViAmgLgG+ZqW2pqXmtVaurVtPNavXy5Yvnz188f/7s7Oxsc2Vcb9abcTNvxqIFYMDc3GEgEEHAJMJCQSSl1DRt23WLRTf0fdf3TdO2/bBc7C4WwzAsl8udnd3dYdG3bRskfPsfXOMT/2S8ffTW4s+8B3IC6Cs//0sADIZfR91tq2qpdc45z1MuuZQ6z9M8jptxvV5das2ANynuLBd91zYhMBPMQpC+73Z2dvcO9uB+fHyS8xQkOizP8zRtzlereZqqagjS9X3btikmIjFVA0Qkpa7r+5haZmEiAzOJSIBzLqq1lFIBNEmsls3m0momgZvWrEWLqZJwjCmGABY3r1VL1VJ1vVm/PDk5Ozs7PT8f5wzwPJfVenVxufrF7i/iE5/4p8fX//JNwD/6ytcBGAy/jrrbVtVS65xznqdccil1nqd5HDfjer261JoBb1LcWS76rm1CYCaYhSB93+3s7O4d7MH9+Pgk5ylIdFie52nanK9W8zRV1RCk6/u2bVNMRGKqBohISl3X9zG1zMJEBmYSkQDnXFRrKaUCaJJYLZvNpdVMAjetWYsWUyXhGFMMASxuXquWqqXqerN+eXJydnZ2en4+zhngeS6r9ericnV5sd5MUy1lznm9GatqkJCiNG0isGlRc8AAMDMJAXD3agoDmJg5BBEJgQOLsMhiMdy6devevftvvPGpu/fuHR7e7LveHAAziQTp2iYEMXKQM4GYQCTExETMDBARiAiEf4BAzAwY/l8YRACYsGXu2HK4E0DYsqqW52m13qxXq4vLi816M07jNI6bcWszT1Op2dzNzKsanAnExB8TCTHGpklt03aLvu+7ru3atmm7YbnY6bpFiFupiUmCMDOA3/T7z/CJfzJ+5UcPYgwGB5y++gu/DMBwhYmJABYQmMgAuOuWaVU1Va1aa5nmcVxv5nksJcOUBWxWa6m5lDwzo23b5XLY3983s+Pjk5xnCQHuueRpGlfr1ZwzDLFJi2HoF33XtEFEHTGm5TB0/dC2XYyJmIgYIJBESSA2NTU3VQbFJhJUS4YVFoZbqVuqpswSYxAREKujFt0y11J1znm9GS8vVuOcDTaN88Xl5enZ+enJ6fnl5TSOq/V4uVrNORMhsIQoTGSqgAPEwhyEmZgYgMEYIGaWK6lJi8XQdV2TmsViuX9wcHh44+bNm7t7B0M/hJgc14xEWK4wGCCAnAlMDCJiApGAsEUAiHDFARAIv5HjCjlAuEZEAIjJ1cxrVTN3gzu23B1QMzevqrUUs1pN3dTNzAE4tghbjCuGKwwQEzHHIDGGEKJIiDE1TR8kETGImMjcAbiZucMcAAsTszCziNY6jqO5pxSFGW6llM04rdbri4vLi/Pz09OTy8tVzoWYUmqEqdS62WzOTk/nnJuUurZb7u6EEOZ5LqXUWoNIv1h0Xde2LbNoVcBEJKXU94umbUKIRDBVEMUQQwwpJmLWWmpVMze1Umu5UsucS825FK21lFJrLVu1ml4BIBJUddxs5pIBMBGLuLvWamZqBnczK7XM8xRD2NvfH4ah6xoJwQzuBripmbrBAA8iKbUiXLXGGHeGnaZtJIgwA9R23eH+/u7e7rBYhhRhSsJNCiyMK/7ul74KwHCFiYkAFhCYyAC465ZpVTVVrVprmeZxXG/meSwlw5QFbFZrqbmUPDOjbdvlctjf3zez4+OTnGcJAe655GkaV+vVnDMMsUmLYegXfde0QUQdMablMHT90LZdjImYiBggkERJIDY1NTdVBsUmElRLhhUWhlupW6qmzBJjEBEQq6MW3TLXUnXOeb0ZLy9W45wNNo3zxeXl6dn56cnp+eXlNI6r9Xi5Ws05EyGwhChMZKqAA8TCHISZmBiAwRggZpYrqUmLxdB1XZOaxWK5f3BweHjj5s2bu3sHQz+EmBzXjERYrjAYIICcCUwMImICkYCwRQCIcMUBEAi/keMKOUC4RkQAiMnVzGtVM3eDO7bcHVAzN6+qtRSzWk3d1M3MATi2CFuMK4YrDBATMccgMYYQokiIMTVNHyQRMYiYyNwBuJm5wxwACxOzMLOI1jqOo7mnFIUZbqWUzTit1uuLi8uL8/PT05PLy1XOhZhSaoSp1LrZbM5OT+ecm5S6tlvu7oQQ5nkupdRag0i/WHRd17Yts2hVwEQkpdT3i6ZtQohEMFUQxRBDDCkmYtZaalUzN7VSa7lSy5xLzbkUrbWUUmstW7WaXgEgElR13GzmkgEwEYu4u9ZqZmoGdzMrtczzFEPY298fhqHrGgnBDO4GuKmZusEADyIptSJctcYYd4adpm0kiDAD1Hbd4f7+7t7usFiGFGFKwk0KLIwrTu9+6WuObyIiBhMJCzMxExGYHID/KjNztVrLOI8lZy1ZS1bTWnKexjxPZZ4Ajyn2fb+z3DG305PzOWeJkQHVMud5HCfVCkJMaVgMbd91TcMi5h5TWg7Lru1iaokF19xBEOEAYvMrcAhRiFEY8MrkzEyA1qpm1ZSJJQQRAZG6a7UtAAaYI+eyWW9yyQaUXFbrzfnFxdn5+Wq1msdpM47rzSbn4u5EJMJC5G4AiIiFmYUDEzMTDM4gZgJfSSl1fde2Xdd2fTcsl0O/GLq+b5o2hsgsAGPLCdeIIUJEcAIRiIhBYNAWCN9EgAMgEK44rhB+HXcHObacsMVETFtwdzV1NXcjIpaQQpAYhQmAu5s7AGYAbu5wx69xwLfMHVvuAMycGSAAxMREIhwJ7CDAt8wdfsUcgAPERMQsTAQyt5ILGE2TogQwmVktdZrncZzWq8uz8/PV5WaeJ8AlBHcvuaw3m4uLC60lpqZrm7btAF+tx5KnqhZDXC6Hvu9iSsLsji1miTF0XRtTiiERwQECpRRjDCklIrYr7g43U7WitZZaSi5Xask5l3ytlJyLVq0KBwcx1XEay5xV1dwAmFqpVU3hbtdKKfM8xhD3Dw+Wy6FtG2bRqqpVTbWqqRsM8CAhpVaEq5YY4s7ObtM0MQozE0vf9wcH+7u7u8NiCDECzkCITExwB/DRV99zfBMRMZhIWJiJmYjA5AD8V5mZq9VaxnksOWvJWrKa1pLzNOZ5KvMEeEyx7/ud5Y65nZ6czzlLjAyoljnP4zipVhBiSsNiaPuuaxoWMfeY0nJYdm0XU0ssuOYOgggHEJtfgUOIQozCgFcmZ2YCtFY1q6ZMLCGICIjUXattATDAHDmXzXqTSzag5LJab84vLs7Oz1er1TxOm3FcbzY5F3cnIhEWIncDQEQszCwcmJiZYHAGMRP4Skqp67u27bq267thuRz6xdD1fdO0MURmARhbTrhGDBEighOIQEQMAoO2QPgmAhwAgXDFcYXw67g7yLHlhC0mYtqCu6upq7kbEbGEFILEKEwA3N3cATADcHOHO36NA75l7thyB2DmzAABICYmEuFIYAcBvmXu8CvmABwgJiJmYSKQuZVcwGiaFCWAycxqqdM8j+O0Xl2enZ+vLjfzPAEuIbh7yWW92VxcXGgtMTVd27RtB/hqPZY8VbUY4nI59H0XUxJmd2wxS4yh69qYUgyJCA4QKKUYY0gpEbFdcXe4maoVrbXUUnK5UkvOueRrpeRctGpVODiIqY7TWOasquYGwNRKrWoKd7tWSpnnMYa4f3iwXA5t2zCLVlWtaqpVTd1ggAcJKbUiXLXEEHd2dpumiVGYmVj6vj842N/d3R0WQ4gRcAZCZGKCOwB6+PWPADjhihOY4gfnxgAAFw9JREFUhYmYiZiZwAQigHDFzdxN1Q1ugMEdbm5qWmvOteRaMoAYJKbUtp2qXq5WpaiEwFuAmW4ZjIklSGxSiikEYSJzBxELMwuTmMPUqpqqqYFADsBxxUmYYxBmAiwwhSAEuJm6qTmIZIvFCQa4OgBidsDMcynzNKtZCOKOXGvOeZ5zLaqmVVWrVlUARBAWIrgbACImgrCQgIhAcDgAAhExM7bUTERS27apSSmJBHcCYE4gYmKAATd3mIEQhIkBAhMIICIQgUAgbBEBYGwRCFccRAARAAIB7oC7w90cIGwxiAkgCiIhBHcvuZgbE7FcAbzUCnODM7MIM5H5lllVg+EaM0DERMTMRPhVbu5u2DKDmcMJIBAxACamKyACiAkAAY5rRBSCMBGYzUxLrapqauYA3KyUXLfUmEhCAKBV53ler9dqGmMMIcUgpZbLy1XOMxwxpmHoU2qJ4YZai5kDxizxShAJxAx3EISZRYIEYcYVAsh8y9TcTaua1Vq1bpVSy5VaSq61lKJVq1YtNW+VolqL6v/TFtwoSZJVxxr93M+JrO6ZkQbp/R9OZlf3R2ASJgMElRHb/UZW9cDQxlpzzct1XZkpTZrMnNf78ynr69cvX7+8PR4P0Hmdc17nnNc1N1qo1357e5M0cx3H4zc///zDDz8ct8fj7fH46aeffvMv//rzP//09esPa++2wLIQSdr+4be/BypeKuxlyZZsCwsJxEuTNjMNDYSWppnMdT2f1/m8zidw7HU8Hl++fJ2ZP/zxj+c5a2/fIJlbiOW11/H2eByPvZeltEhetpe1UjK5JjOZIFSgvFTLPvayBdnW3kvQZJpJkdbNqyLQKSC7kPR5nu9/eZ9k79XyvK7n8/n+/rzOmcw1M9dcM4DE8pJoA0iWWF5aSEKUAkKSbW6TrLUeX758ebw9Ho+1disgFZJlMDQtCWIvyyAsBJKQEELcJMDchHgpEkiAELTQljYFcTOyQNpr7b3bns8zjSWvF+h5XaShtteypfSWXBPCBxskS7It8U3TNtwSklKBkAxY1gsSyAIE5YOkvZcl7CRzXtfMZJICTc7zed0mltbewFzz/v7+pz/9aTLHcez9OPY6r/MPf/jj8/lOOY7HTz/98Hh8kWm4rjMpxF7Hy15ry6ZFLNtr7bWXzYtA6S2TNnNNcl3XXLfzvM6X6zyf13We51xzzTXn9byd58x1zsw1L9d1ZaY0aTJzXu/Pp6yvX798/fL2eDxA53XOeZ1zXtfcaKFe++3tTdLMdRyP3/z88w8//HDcHo+3x+Onn376zb/868///NPXrz+svdsCy0Ikaavf/tv/CyBAIGRbSJawQFjiplJCKLA2y5IQbUImcyXTRBRp2V6r6fv7OYm8bC8JQUEgsLxsvQCllN6AknQm1zXXZNKWlwJCXvJaXkawl469hUqSpuHmBQJKkyIvL0zTa+Z8Plv23rZLJ81Miz70Ay+SAEF5kYQMkhFqaMtNwtIk5/kEjsdjrW0ZOCeZTMpNSxLQQoK6LRmEwICQBBIg8SIDFi8lYCFZCCEKaUlJMR9kvay1HscDeH++z4wkpGW3Pa8zCWB73ey2adpJS8tNWMhespYlAb1NpmmawgDCAllIRrJkmZvFLQVKZR1raaklk/M8ZwKVtI+9bCBtB6y1llHInNef39+bHMeWl6Xz+fzj//zpOk/k49hfv3z1cibJTNpkEmDZXmuvZTmUW4tke91sUFsg5Zt28tJkMpnruuaaa64kc57n8/m8Xua6JpnrmvM6M59aQptkZs7zgqzltfexD+D5/Mt5Xtd1XtdMJil0r3U83izNzHEcP/z409evb8c+3r68ffny5ccff/rNz//0ww8/Pt4e9qKFIgFJ2r7//o8BBAiEbAvJEhYIS9xUSggF1mZZEqJNyGSuZJqIIi3bazV9fz8nkZftJSEoCASWl60XoJTSG1CSzuS65ppM2vJSQMhLXsvLCPbSsbdQSdI03LxAQGlS5OWFaXrNnM9ny97bdumkmWnRh37gRRIgKC+SkEEyQg1tuUlYmuQ8n8DxeKy1LQPnJJNJuWlJAlpIULclgxAYEJJAAiReZMDipQQsJAshRCEtKSnmg6yXtdbjeADvz/eZkYS07LbndSYBbK+b3TZNO2lpuQkL2UvWsiSgt8k0TVMYQFggC8lIlixzs7ilQKmsYy0ttWRynudMoJL2sZcNpO2AtdYyCpnz+vP7e5Pj2PKydD6ff/yfP13niXwc++uXr17OJJlJm0wCLNtr7bUsh3JrkWyvmw1qC6R8005emkwmc13XXHPNlWTO83w+n9fLXNckc11zXmfmU0tok8zMeV6Qtbz2PvYBPJ9/Oc/rus7rmskkhe61jsebpZk5juOHH3/6+vXt2Mfbl7cvX778+ONPv/n5n3744cfH28NetFAkIElb/fZ//ZabZAmMhAUSFEKBlFtbSZa9JMsqlCaNG4GEBLRpJ9OCrAUOL+ZvQofQ3CAUBJK9jAKUlLaTAi2gG8halhAGq0s+HttS2klyTSi47ZW0mdby3ss2KMnzPNsuL0tAm+saQLb10nYSyj+g8qEUeuOlfBLoGxASCGiFBJIs2ZKQXBKoAfPJEpIAiZtkQOLW8kE3hIV4KaR8x1q21yI9zzMNYBmL9MrVRBLS9sIixawlIG0TCDe9WLaVFDozeanl4/FYa7UiDVDSApb4JAFG0DTXdbX1MogUS/K21rYtRNI5EzDC2kuTPp8n6dobyXBlnu/PNGut7bWOlfT5/p72cRzCV66mgOxtY4GazExbSbbX3kAmbXXDWDeLD0WF0qYvpGnOK0mhTSfN5JppMgkt0DZt0mZuENnHXkne39/P80w6mcxM0nTvdTwe1poZiX3sYx97r+N4PN7evn59+/r1y+Px2MtIFCgIQQv8/j9+z02yBEbCAgkKoUDKra0ky16SZRVKk8aNQEIC2rSTaUHWAocX8zehQ2huEAoCyV5GAUpK20mBFtANZC1LCIPVJR+PbSntJLkmFNz2StpMa3nvZRuU5HmebZeXJaDNdQ0g23ppOwnlH1D5UAq98VI+CfQNCAkEtEICSZZsSUguCdSA+WQJSYDETTIgcWv5oBvCQrwUUr5jLdtrkZ7nmQawjEV65WoiCWl7YZFi1hKQtgmEm14s20oKnZm81PLxeKy1WpEGKGkBS3ySACNomuu62noZRIoleVtr2xYi6ZwJGGHtpUmfz5N07Y1kuDLP92eatdb2WsdK+nx/T/s4DuErV1NA9raxQE1mpq0k22tvIJO2umGsm8WHokJp0xfSNOeVpNCmk2ZyzTSZhBZomzZpMzeI7GOvJO/v7+d5Jp1MZiZpuvc6Hg9rzYzEPvaxj73XcTweb29fv759/frl8XjsZSQKFISgBfTbf/8dYN2MDcJCAtomDaW0TLustfdahkLJtEljdfmmtcRtMpPrGvA+3tBq0pbyogZoJ5NOG1qoJCTLoFBeBOJTASGDjHkp1LCWj72xSKftzBSklsykSStp7b2WQW2v66IFW+LWzEypZVuWgSvTtBUgQPyipaFQaIGWlyLttRCTtAXdlpdsJFCLMPJtWTaZMYXyQQIkhAEBkgBLvDTlZgkBMuJDqPmOuJmEdNpKsmQUmqaNEJJlIK3FcSxJkzQvUJANeinQZpK2qezjOLYNamlJgUKBFpDEBwGdOc9nqdfyDWEt34QkWnq7phRJtmxRzmsIWrYEpO0MaG3fkDLXX/78Xni8va3lmckkLciWLRDplauTItnHWqAr0yLJErItCUuAVAnEr4gCQlASJk1CCwVSoDdSaMkkwLIneb6/X3MBTSdpJont4/GQ3ExB0vLL3vvYxzr249iyxUtBvNhIAn73f/4TsG7GBmEhAW2ThlJapl3W2nstQ6Fk2qSxunzTWuI2mcl1DXgfb2g1aUt5UQO0k0mnDS1UEpJlUCgvAvGpgJBBxrwUaljLx95YpNN2ZgpSS2bSpJW09l7LoLbXddGCLXFrZqbUsi3LwJVp2goQIH7R0lAotEDLS5H2WohJ2oJuy0s2EqhFGPm2LJvMmEL5IAESwoAASYAlXppys4QAGfEh1HxH3ExCOm0lWTIKTdNGCMkykNbiOJakSZoXKMgGvRRoM0nbVPZxHNsGtbSkQKFAC0jig4DOnOez1Gv5hrCWb0ISLb1dU4okW7Yo5zUELVsC0nYGtLZvSJnrL39+Lzze3tbyzGSSFmTLFoj0ytVJkexjLdCVaZFkCdmWhCVAqgTiV0QBISgJkyahhQIp0BsptGQSYNmTPN/fr7mAppM0k8T28XhIbqYgafll733sYx37cWzZ4qUgXmwkAfrtv/8OsCQbLSOWIoFopwUSoKVaPvZhK5lmkkmH1NZaWpaXLSgZ2hRLBziTpE2hUG4uQiqSJUuyml65ZnID7IUE5tYm2AYBSZuBCm2vtZfttLRpkZYX0qQ0bWXtvZAoTaehRTaSaDJzJQWtpb0XcF3TQQiEhfhQKCo3FcpNfCPt5bbXLaHYXsexbWTgmhBAWHtZQGMK5a8kvpG5CQOSaIESML+QxAdZfGfaTLhZlmSWbtxK+VSmNKSx2GtJatM2bbkVWBZCiF+0TJprKJa1vGxJCPFSoPSF9IZUCwlZIH6lL5kMLVjy3gYBTa8MBXRb1o2bkGUETHKeF2HtLavNJEwDNpKWFzBNc8NoHwvUNuXX2hKgUL5jSV7WDWg7AQpY3FJaSHGXBZ1wk2nyfH9Os2RZTacz14X02A9ZTWXWWrZAINkW3xGgl7V0+93//k/Akmy0jFiKBKKdFkiAlmr52IetZJpJJh1SW2tpWV62oGRoUywd4EySNoVCubkIqUiWLMlqeuWayQ2wFxKYW5tgGwQkbQYqtL3WXrbT0qZFWl5Ik9K0lbX3QqI0nYYW2UiiycyVFLSW9l7AdU0HIRAW4kOhqNxUKDfxjbSX2163hGJ7Hce2kYFrQgBh7WUBjSmUv5L4RuYmDEiiBUrA/EISH2TxnWkz4WZZklm6cSvlU5nSkMZiryWpTdu05VZgWQghftEyaa6hWNbysiUhxEuB0hfSG1ItJGSB+JW+ZDK0YMl7GwQ0vTIU0G1ZN25ClhEwyXlehLW3rDaTMA3YSFpewDTNDaN9LFDblF9rS4BC+Y4leVk3oO0EKGBxS2khxV0WdMJNpsnz/TnNkmU1nc5cF9JjP2Q1lVlr2QKBZFt8R4Be1tLLf/7f/0IyCMvGqgQqAkKB8hJqe60lMZlmaGglEMsSIG4CIyTq6SLqh7S0UATCwhJLS0IyhF5zXTOZCVi2LJkyLWBZCJgmV6DGXl42UlIoN3nvhdxbmsaStyU1vZFifbLI7ZpJaJe9j21xXtMAEkICIaBQKBQjcavKB0m2Sa65JmmRvI+91rKdkqRpi26WJbdQKL8iiV+xBEgC2vIhLR8sAZIASUBbPrSdJLCWlyWEIcXcJEAtTVsmkViWEFDKh7bcVIEsbpIQKJPn+0lree1lW5LNp4QC/QRUQpaQTbi1AdpyazMZWrDsvSyroe0klBZJy5IlhGkK5UNbEBVCou1Mb1BZey0Q9CXcvAw03Fr+Kn2BkvIda9m6WUDTlpuELG4laVoLLwkVKKhtns8TuvZeNvSaXNcF7L2XndbS2rY0U14E/cQHiZabJC9b/P4//hvJICwbqxKoCAgFykuo7bWWxGSaoaGVQCxLgLgJjJCop4uoH9LSQhEIC0ssLQnJEHrNdc1kJmDZsmTKtIBlIWCaXIEae3nZSEmh3OS9F3JvaRpL3pbU9EaK9ckit2smoV32PrbFeU0DSAgJhIBCoVCMxK0qHyTZJrnmmqRF8j72Wst2SpKmLbpZltxCofyKJH7FEiAJaMuHtHywBEgCJAFt+dB2ksBaXpYQhhRzkwC1NG2ZRGJZQkApH9pyUwWyuElCoEye7yet5bWXbUk2nxIK9BNQCVlCNuHWBmjLrc1kaMGy97KshraTUFokLUuWEKYplA9tQVQIibYzvUFl7bVA0Jdw8zLQcGv5q/QFSsp3rGXrZgFNW24SsriVpGktvCRUoKC2eT5P6Np72dBrcl0XsPdedlpLa9vSTHkR9BMfJFpukrxsoT/+15+4CRAIFCg3gTBICISgkDYtDWLby7JBUJLMdXYis2yvA9Y1NJIwL6EtL0LC4lOaZlDxor2ua1pLwiBQW4S8ll5orynFkhFLtJmkBWx5LWTaTKcDLBkrCWDLkqxtSXRynpNbu+3j2IhMJqXmRTegFALlZiQQUERbYEmhTdIGWbIt35altJSUptBlWVoCCqTlgyX+niRAEhRoaZuWD5YAfeAXbYFSJAssoOk1gUpalixQw4feJG5CmG9CKaQUEJKFJETJlFaWkC0+JPSFTxIIQWkSwFJhkraApL2WTFNoEkAfwHwqaSiyBEhtr7kyBbx07A1KAtgGkk6SiZeOvWVDgaYtEi0zbZFk3ZAE5ZvyHQkQskloablJSNxakk4icWzbmmmL1DTnedkcxyEbaHKeF7DWst1WQjaQmRbbID7IfGp6AyQB73945yZAIFCg3ATCICEQgkLatDSIbS/LBkFJMtfZicyyvQ5Y19BIwryEtrwICYtPaZpBxYv2uq5pLQmDQG0R8lp6ob2mFEtGLNFmkhaw5bWQaTOdDrBkrCSALUuytiXRyXlObu22j2MjMpmUmhfdgFIIlJuRQEARbYElhTZJG2TJtnxbltJSUppCl2VpCSiQlg+W+HuSAElQoKVtWj5YAvSBX7QFSpEssICm1wQqaVmyQA0fepO4CWG+CaWQUkBIFpIQJVNaWUK2+JDQFz5JIASlSQBLhUnaApL2WjJNoUkAfQDzqaShyBIgtb3myhTw0rE3KAlgG0g6SSZeOvaWDQWatki0zLRFknVDEpRvynckQMgmoaXlJiFxa0k6icSxbWumLVLTnOdlcxyHbKDJeV7AWst2WwnZQGZabIP4IPOp6Q2QBOjP//0XoOKlAkUNUCFZQsJCWKQ9r0mCavtYy8t7IdHhmut8vmdGsPc6jjdY59kpyzLCEMLf2ECTTiZzAus4sM7zbCoEwgKRgtZekpaVkJSiDxaTZiYtYMl7IVGuK5MBlg20AdZatpbkxTJJz3Nypa2t49hCk2TaCnTjRaUwUBBGwmpUoC0g0Za2UPSNb8sSgpKStIng2F4SFGjLPyIJkMRNvLRJ0/IL6xt+0Zabbmhxa8nkvIbgJdnbApUXQelNQghL4tZCWqY0rSUh2ZKMUgg2Ep9akiZtuUnIErJJcs60XTYwM0kBLx/7ZmCSzLQF9LJAspq2aSsJkJTkPM9JgOX19nbImmsAe8nKZJK5xtZxHLItoDPlQ9NrSsBa1ieE+Ds235FomaHlJrEWtxmSTiJxbNua6Q11krnGS8c+9jYwyXlO272WZCggq2kyKXstkCzAQuLWdqY3PsyfB6h4qUBRA1RIlpCwEBZpz2uSoNo+1vLyXkh0uOY6n++ZEey9juMN1nl2yrKMMITwNzbQpJPJnMA6DqzzPJsKgbBApKC1l6RlJSSl6IPFpJlJC1jyXkiU68pkgGUDbYC1lq0lebFM0vOcXGlr6zi20CSZtgLdeFEpDBSEkbAaFWgLSLSlLRR949uyhKCkJG0iOLaXBAXa8o9IAiRxEy9t0rT8wvqGX7Tlphta3FoyOa8heEn2tkDlRVB6kxDCkri1kJYpTWtJSLYkoxSCjcSnlqRJW24SsoRskpwzbZcNzExSwMvHvhmYJDNtAb0skKymbdpKAiQlOc9zEmB5vb0dsuYawF6yMplkrrF1HIdsC+hM+dD0mhKwlvUJIf6OzXckWmZouUmsxW2GpJNIHNu2ZnpDnWSu8dKxj70NTHKe03avJRkKyGqaTMpeCyQLsJC4tZ3pjQ//HyN/Tdqbo4GoAAAAAElFTkSuQmCC",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='infer_results_cats_dogs_model/EfficientBBoxHead-boundingbox_BBoxVisualizer-boundingbox_7.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On the left above, we can see the annotation; on the right, we can see the prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name =\"export-and-archive\"></a>\n",
    "\n",
    "## 🗂️ Export and Archive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the model is trained and tested, we want to prepare it for deployment on the device. This preparation consists of 2 steps. First, we want to export the model trained with PyTorch to a more general format called [`Open Neural Network Exchange (ONNX)`](https://onnx.ai/). Then, we want to package this exported model with all the metadata containing information about the inputs, outputs, and training configuration used. This is called archiving. These steps can be done quickly with just one command in `LuxonisTrain`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "archieve_path = luxonis_model.archive(weights=weights)\n",
    "print(\"Model archieved to:\", archieve_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that two new folders were created in our run directory. One is called `export` and has an ONNX model while the other is called `archive` which has `.tar.xz` file. The tar file is a compressed file that holds the aforementioned ONNX model with all the model metadata."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name =\"deploy\"></a>\n",
    "\n",
    "## 🤖 Deploy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have successfully exported and archived the model, we aim to deploy it to the Luxonis device. The model's specific format depends on the Luxonis device series you have. We will show you how to use our [`ModelConverter`](https://github.com/luxonis/modelconverter) to convert the model as simply as possible.\n",
    "\n",
    "We'll start by installing the `ModelConverter`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -q modelconv@git+https://github.com/luxonis/modelconverter.git@main -U"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use `ModelConverter`'s Python API, which utilizes our [`HubAI`](https://hub.luxonis.com) to convert the model in the background. To start with the conversion, you need to create an account on the `HubAI` platform and obtain the API key for your team.\n",
    "\n",
    "To log in to HubAI, use the following command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!modelconverter hub login"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To convert the model, we can use either CLI or Python API. We'll use the latter. For more information, please look [here](https://github.com/luxonis/modelconverter?tab=readme-ov-file#online-usage).\n",
    "\n",
    "The call below will create a new model card inside your team on `HubAI` with the model file and details uploaded. It will further convert the model on the cloud to the selected target platform (e.g. [`RVC2`](https://rvc4.docs.luxonis.com/hardware/platform/rvc/rvc2/), [`RVC4`](https://rvc4.docs.luxonis.com/hardware/platform/rvc/rvc4/)) and download the converted model to your device. Choosing the target is as simple as setting a `target` argument in the `convert` function.\n",
    "\n",
    "Besides this, there are some platform-specific parameters. To check them out, please visit our [documentation](https://rvc4.docs.luxonis.com/software/ai-inference/conversion/rvc-conversion/offline/modelconverter/#ModelConverter-Parameters-Platform-Specific)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "NN_ARCHIVE_PATH = \"<YOUR_MODEL_ARCHIVE_PATH>\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Model <span style=\"color: #008000; text-decoration-color: #008000\">'Cats&amp;Dogs Object Detection Model'</span> created with ID <span style=\"color: #008000; text-decoration-color: #008000\">'fd8dd235-d0cd-487b-b5f6-8760c5980f91'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Model \u001b[32m'Cats&Dogs Object Detection Model'\u001b[0m created with ID \u001b[32m'fd8dd235-d0cd-487b-b5f6-8760c5980f91'\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Model variant <span style=\"color: #008000; text-decoration-color: #008000\">'Cats&amp;Dogs Object Detection Model 320x320'</span> created with ID <span style=\"color: #008000; text-decoration-color: #008000\">'6b97a5d5-6aba-480d-86f1-cb450b58d51a'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Model variant \u001b[32m'Cats&Dogs Object Detection Model 320x320'\u001b[0m created with ID \u001b[32m'6b97a5d5-6aba-480d-86f1-cb450b58d51a'\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Model instance <span style=\"color: #008000; text-decoration-color: #008000\">'Cats&amp;Dogs Object Detection Model 320x320 base instance'</span> created with ID \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'8fbd5c59-7ec7-4e0d-b3a6-f2d717fd3323'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Model instance \u001b[32m'Cats&Dogs Object Detection Model 320x320 base instance'\u001b[0m created with ID \n",
       "\u001b[32m'8fbd5c59-7ec7-4e0d-b3a6-f2d717fd3323'\u001b[0m\n"
      ]
     },
     "metadata": {},
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">File <span style=\"color: #008000; text-decoration-color: #008000\">'cat_dog_detection_model.onnx.tar.xz'</span> uploaded to model instance <span style=\"color: #008000; text-decoration-color: #008000\">'8fbd5c59-7ec7-4e0d-b3a6-f2d717fd3323'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "File \u001b[32m'cat_dog_detection_model.onnx.tar.xz'\u001b[0m uploaded to model instance \u001b[32m'8fbd5c59-7ec7-4e0d-b3a6-f2d717fd3323'\u001b[0m\n"
      ]
     },
     "metadata": {},
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Model instance <span style=\"color: #008000; text-decoration-color: #008000\">'Cats&amp;Dogs Object Detection Model 320x320 exported to rvc2'</span> created for rvc2 export with ID \n",
       "<span style=\"color: #008000; text-decoration-color: #008000\">'6e45a8b7-4dc8-4cb1-8d0a-9d758c6949c7'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Model instance \u001b[32m'Cats&Dogs Object Detection Model 320x320 exported to rvc2'\u001b[0m created for rvc2 export with ID \n",
       "\u001b[32m'6e45a8b7-4dc8-4cb1-8d0a-9d758c6949c7'\u001b[0m\n"
      ]
     },
     "metadata": {},
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">/Users/jancuhel/miniconda3/envs/luxonis-tutorials/lib/python3.11/site-packages/rich/live.py:231: UserWarning: \n",
       "install \"ipywidgets\" for Jupyter support\n",
       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n",
       "</pre>\n"
      ],
      "text/plain": [
       "/Users/jancuhel/miniconda3/envs/luxonis-tutorials/lib/python3.11/site-packages/rich/live.py:231: UserWarning: \n",
       "install \"ipywidgets\" for Jupyter support\n",
       "  warnings.warn('install \"ipywidgets\" for Jupyter support')\n"
      ]
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     "metadata": {},
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Donwloaded <span style=\"color: #008000; text-decoration-color: #008000\">'cats-dogs-object-detection-model-320x320-exported-to-rvc2/cat_dog_detection_model.rvc2.tar.xz'</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "Donwloaded \u001b[32m'cats-dogs-object-detection-model-320x320-exported-to-rvc2/cat_dog_detection_model.rvc2.tar.xz'\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from modelconverter import convert\n",
    "\n",
    "converted_model = convert(\n",
    "    \"rvc2\",\n",
    "    path=NN_ARCHIVE_PATH,\n",
    "    name=\"Cats&Dogs Object Detection Model\",\n",
    "    description_short=\"Trained object detection model on the cats and dogs detection dataset.\",\n",
    "    tasks=[\"OBJECT_DETECTION\"],\n",
    "    license_type=\"MIT\",\n",
    "    is_public=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have successfully converted our trained model for an RVC2 device, so let's test it! Please copy the path to the downloaded archive with the converted model from the output log of the last code cell; we will use it in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "MODEL_PATH = \"cats-dogs-object-detection-model-320x320-exported-to-rvc2/cat_dog_detection_model.rvc2.tar.xz\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check out other possible ways to convert your model for our devices, please refer to our [documentation](https://rvc4.docs.luxonis.com/software/ai-inference/conversion/rvc-conversion/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name =\"depthai-script\"></a>\n",
    "\n",
    "## 📷 DepthAI Script"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test our model on one of our cameras, we first need to install [`DepthAI`](https://rvc4.docs.luxonis.com/software/) in version 3 and [`DepthAI Nodes`](https://rvc4.docs.luxonis.com/software/ai-inference/depthai-nodes/). Moreover, the script we'll write must run locally and require a Luxonis device connected to your machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --extra-index-url https://artifacts.luxonis.com/artifactory/luxonis-python-release-local/ depthai==3.0.0a10\n",
    "%pip install -q depthai-nodes@git+https://github.com/luxonis/depthai-nodes.git@main -U"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the script to run the model on the DepthAI device:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import depthai as dai\n",
    "from depthai_nodes import ParsingNeuralNetwork\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def visualize_detections_xyxy(frame: np.ndarray, message: dai.ImgDetections):\n",
    "    \"\"\"Visualize the detections on the frame.\n",
    "\n",
    "    The detections are in xyxy format (dai.ImgDetections).\n",
    "    \"\"\"\n",
    "    labels = [\"cat\", \"dog\"]\n",
    "    detections = message.detections\n",
    "    for detection in detections:\n",
    "        xmin, ymin, xmax, ymax = (\n",
    "            detection.xmin,\n",
    "            detection.ymin,\n",
    "            detection.xmax,\n",
    "            detection.ymax,\n",
    "        )\n",
    "        if xmin > 1 or ymin > 1 or xmax > 1 or ymax > 1:\n",
    "            xmin = int(xmin)\n",
    "            ymin = int(ymin)\n",
    "            xmax = int(xmax)\n",
    "            ymax = int(ymax)\n",
    "        else:\n",
    "            xmin = int(xmin * frame.shape[1])\n",
    "            ymin = int(ymin * frame.shape[0])\n",
    "            xmax = int(xmax * frame.shape[1])\n",
    "            ymax = int(ymax * frame.shape[0])\n",
    "        cv2.rectangle(\n",
    "            frame, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (255, 0, 0), 2\n",
    "        )\n",
    "\n",
    "        cv2.putText(\n",
    "            frame,\n",
    "            f\"{detection.confidence * 100:.2f}%\",\n",
    "            (int(xmin) + 10, int(ymin) + 20),\n",
    "            cv2.FONT_HERSHEY_TRIPLEX,\n",
    "            0.5,\n",
    "            (255, 0, 0),\n",
    "        )\n",
    "        if labels is not None:\n",
    "            cv2.putText(\n",
    "                frame,\n",
    "                labels[detection.label],\n",
    "                (int(xmin) + 10, int(ymin) + 40),\n",
    "                cv2.FONT_HERSHEY_TRIPLEX,\n",
    "                0.5,\n",
    "                (255, 0, 0),\n",
    "            )\n",
    "\n",
    "    cv2.imshow(\"Detections\", frame)\n",
    "    if cv2.waitKey(1) == ord(\"q\"):\n",
    "        cv2.destroyAllWindows()\n",
    "        return True\n",
    "\n",
    "    return False\n",
    "\n",
    "\n",
    "with dai.Pipeline() as pipeline:\n",
    "    cam = pipeline.create(dai.node.Camera).build()\n",
    "    nn_archive = dai.NNArchive(MODEL_PATH)\n",
    "    # Create the neural network node\n",
    "    nn_with_parser = pipeline.create(ParsingNeuralNetwork).build(\n",
    "        cam.requestOutput((320, 320), type=dai.ImgFrame.Type.BGR888p, fps=30), \n",
    "        nn_archive\n",
    "    )\n",
    "    # Create output queues\n",
    "    parser_output_queue = nn_with_parser.out.createOutputQueue()\n",
    "    frame_queue = nn_with_parser.passthrough.createOutputQueue()\n",
    "\n",
    "    # Start pipeline\n",
    "    pipeline.start()\n",
    "\n",
    "    while pipeline.isRunning():\n",
    "        # Get the frame\n",
    "        frame: dai.ImgFrame = frame_queue.get().getCvFrame()\n",
    "        # Get the parsed message containing the bounding boxes\n",
    "        parser_msg: dai.ImgDetections = parser_output_queue.get()\n",
    "        if visualize_detections_xyxy(frame, parser_msg):\n",
    "            pipeline.stop()\n",
    "            break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yay! 🎉🎉🎉 Huge congratulations, you have successfully finished this tutorial in which you deployed an object detection model trained using `luxonis-train` on cats and dogs images annotated using `DataDreamer` to our camera!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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